Method and apparatus for normalizing and converting structured content

ABSTRACT

A method and apparatus are disclosed for transforming information from one semantic environment to another. In one implementation, a SOLx system ( 1700 ) includes a Normalization/Translation NorTran Workbench ( 1702 ) and a SOLx server ( 1708 ). The NorTran Workbench ( 1702 ) is used to develop a knowledge base based on information from a source system ( 1712 ), to normalize legacy content ( 1710 ) according to various rules, and to develop a database ( 1706 ) of translated content. During run time, the SOLx server ( 1708 ) receives transmissions from the source system ( 1712 ), normalizes the transmitted content, accesses the database ( 1706 ) of translated content and otherwise translates the normalized content, and reconstructs the transmission to provide substantially real-time transformation of electronic messages.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part of U.S. ProvisionalApplication Ser. No. 60/214,090 entitled “Business InformationLocalization System”, filed on Jun., 26, 2000, which is incorporatedherein by reference.

FIELD OF THE INVENTION

The present invention relates in general to machine transformation ofinformation from one semantic environment to another and, especially, totransformation of structured, locale-specific information such aselectronic forms and other business information.

BACKGROUND OF THE INVENTION

In a number of contexts, including the business-to-business context,there are potential communication difficulties due to different semanticenvironments between the source and target user systems for a givencommunication. Such semantic environments may differ with respect tolinguistics and/or syntax. In this regard, linguistic differences may bedue to the use of different languages or, within a single language, dueto terminology, proprietary names, abbreviations, idiosyncraticphrasings or structures and other matter that is specific to a location,region, business entity or unit, trade, organization or the like(collectively “locale”). Also within the purview of linguisticdifferences for present purposes are different currencies, differentunits of weights and measures and other systematic differences. Syntaxrelates to the phrasing, ordering and organization of terms as well asgrammatic and other rules relating thereto. It will be appreciated thatdifficulties relating to different semantic environments may beexperienced in international communications, interregionalcommunications, interdisciplinary communications, or even incommunications between companies within the same field and country orbetween units of a single enterprise. The emergence of electroniccommerce and economic globalization has heightened the need formachine-based tools to assist in transformation of information, i.e.,manipulation of information with respect to linguistics, syntax andother semantic variations.

Today, such transformation, at least as it relates to thebusiness-to-business context, is largely a service industry. A number ofcompanies specialize in helping companies operate in the globalmarketplace. Among other things, these companies employ translators andother consultants to develop forms, catalogs, product listings, invoicesand other business information (collectively, “business content”) forspecific languages as well as assisting in the handling of incomingbusiness content from different source languages or countries. Suchservices have been indispensable for some businesses, but are laborintensive and expensive. Moreover, the associated processes may entailsignificant delays in information processing or, as a practical matter,have limited capacity for handling information, both of which can beunacceptable in certain business environments. In short, manualtransformation does not scale well.

A number of machine translation tools have been developed to assist inlanguage translation. The simplest of these tools attempt to literallytranslate a given input from a source language into a target language ona word-by-word basis. Specifically, content is input into such a system,the language pair (source-target) is defined, and the literallytranslated content is output. Such literal translation is rarelyaccurate. For example, the term “butterfly valve” is unlikely to beunderstood when literally translated from English to a desired targetlanguage.

More sophisticated machine translation tools attempt to translate wordstrings or sentences so that certain ambiguities can be resolved basedon context. These tools are sometimes used as a starting point for humanor manual translation or are used for “gisting”, which is simply gettingthe gist of the content. However, they tend to be highly inaccurate evenwhen applied for their primary purpose which is to translate standardtext written in common language and in complete sentences conforming tostandard rules of syntax.

Such tools are especially inadequate for use in transforming businesscontent. Such content often is loaded with industry specific technicalterms and jargon, standard and ad hoc abbreviations and misspellings,and often has little or no structure or syntax in its native form.Moreover, the structure of such business content is often composed ofshort item descriptions. Such descriptions are linguistically defined asa “noun phrase”. A noun phrase has one overriding characteristic; it hasno verb. The tendency of machine translation systems to try to createsentences produces unintended results when applied to noun phrases. Forexample, the term “walking shoe” often translates to a shoe that walks.Thus, machine translation tools, though helpful for certain tasks, aregenerally inadequate for a variety of transformation applicationsincluding many practical business content applications.

To summarize, from a practical viewpoint relative to certainapplications, it is fair to state that conventional machine translationdoes not work and manual translation does not scale. The result is thatthe free flow of information between locales or semantic environments issignificantly impeded and, in the context of electronic business, theideal of globalization is far from fully realized.

SUMMARY OF THE INVENTION

The present invention is directed to a computer-based tool andassociated methodology for transforming electronic information so as tofacilitate communications between different semantic environments. In apreferred implementation, the invention is applicable with respect to awide variety of content including sentences, word strings, noun phrases,and abbreviations and can even handle misspellings and idiosyncratic orproprietary descriptors. The invention can also manage content withlittle or no predefined syntax as well as content conforming to standardsyntactic rules. Moreover, the system of the present invention allowsfor substantially real-time transformation of content and handlesbandwidth or content throughputs that support a broad range of practicalapplications. In such applications, the invention provides a system forsemantic transformation that works and scales.

The invention has particular advantages with respect to transformationof business content. For the reasons noted above, transformation ofbusiness content presents special challenges. At the same time the needfor business content transformation is expanding. It has been recognizedthat business content is generally characterized by a high degree ofstructure and reusable “chunks” of content. Such chunks generallyrepresent a core idea, attribute or value related to the businesscontent and may be represented by a character, number, alphanumericstring, word, phrase or the like. The present invention takes advantageof these characteristics to provide a framework by which locale-specificcontent can be standardized as an intermediate step in the process fortransforming the content from a source semantic environment to a targetsemantic environment. Such standardization may encompass linguistics andsyntax as well as any other matters that facilitate transformation. Theresult is that content having little or no syntax is supplied with astandardized syntax that facilitates understanding, the total volume ofunique chunks requiring transformation is reduced, ambiguities areresolved and accuracy is commensurately increased and, in general,substantially real-time communication across semantic boundaries isrealized. Thus, the system of the present invention does not assume thatthe input is fixed or static, but recognizes that the input can be mademore amenable to transformation and that such preprocessing is animportant key to more fully realizing the ideal of globalization withrespect to electronic communications.

According to one aspect of the present invention, a method andcorresponding apparatus are provided for transforming content from afirst semantic environment to a second semantic environment by firstconverting the input data into an intermediate form. The associatedmethod includes the steps of: providing a computer-based device; usingthe device to access input content reflecting the first semanticenvironment and convert at least a portion of the input content into athird semantic environment, thereby defining a converted content; andusing the converted content in transforming a communication between afirst user system operating in the first semantic environment and asecond user system operating in the second semantic environment.

In the context of electronic commerce, the input content may be businesscontent such as a parts listing, invoice, order form, catalogue or thelike. This input content may be expressed in the internal terminologyand syntax (if any) of the source business. In one implementation, thisbusiness content is converted into a standardized content reflectingstandardized terminology and syntax. The resulting standardized contenthas a minimized (reduced) set of content chunks for translation or othertransformation and a defined syntax for assisting in transformation. Theintermediate, converted content is thus readily amenable totransformation. For example, the processed data chunks may be manuallyor automatically translated using the defined syntax to enable rapid andaccurate translation of business documents across language boundaries.

The conversion process is preferably conducted based on a knowledge basedeveloped from analysis of a quantity of information reflecting thefirst semantic environment. For example, this quantity of informationmay be supplied as a database of business content received from abusiness enterprise in its native form. This information is thenintelligently parsed into chunks by a subject matter expert using thecomputer-based tool. The resulting chunks, which may be words, phrases,abbreviations or other semantic elements, can then be mapped tostandardized semantic elements. In general, the set of standardizedelements will be smaller than the set of source elements due toredundancy of designations, misspellings, format variations and the likewithin the source content. Moreover, as noted above, business content isgenerally characterized by a high level of reusable chunks.Consequently, the “transformation matrix” or set of mapping rules isconsiderably compressed in relation to that which would be required fordirect transformation from the first semantic environment to the second.The converted semantic elements can then be assembled in accordance withthe defined syntax to create a converted content that is readilyamenable to manual or at least partially automated translation.

According to another aspect of the present invention, a computer-baseddevice is provided for use in efficiently developing a standardizedsemantic environment corresponding to a source semantic environment. Theassociated method includes the steps of: accessing a database ofinformation reflecting a source semantic environment; using thecomputer-based device to parse at least a portion of the database into aset of source semantic elements and identify individual elements forpotential processing; using the device to select one of the sourceelements and map it to a standardized semantic element; and iterativelyselecting and processing additional source elements until a desiredportion of the source elements are mapped to standardized elements.

In order to allow for more efficient processing, the computer-baseddevice may perform a statistical or other analysis of the sourcedatabase to identify how many times or how often individual elements arepresent, or may otherwise provide information for use in prioritizingelements for mapping to the standardized lexicon. Additionally, thedevice may identify what appear to be variations for expressing the sameor related information to facilitate the mapping process. Such mappingmay be accomplished by associating a source element with a standardizedelement such that, during transformation, appropriate code can beexecuted to replace the source element with the associated standardizedelement. Architecturally, this may involve establishing correspondingtables of a relational database, defining a corresponding XML taggingstructure and/or establishing other definitions and logic for handlingstructured data. It will be appreciated that the “standardization”process need not conform to any industry, syntactic, lexicographic orother preexisting standard, but may merely denote an internal standardfor mapping of elements. Such a standard may be based in whole or inpart on a preexisting standard or may be uniquely defined relative tothe source semantic environment. In any case, once thus configured, thesystem can accurately transform not only known or recognized elements,but also new elements based on the developed knowledge base.

The mapping process may be graphically represented on a user interface.The interface preferably displays, on one or more screens(simultaneously or sequentially), information representing sourcecontent and a workspace for defining standardized elements relative tosource elements. In one implementation, as source elements are mapped tostandardized elements, corresponding status information is graphicallyshown relative to the source content, e.g., by highlighting or otherwiseidentifying those source elements that have been mapped and/or remain tobe mapped. In this manner, an operator can readily select furtherelements for mapping, determine where he is in the mapping process anddetermine that the mapping process is complete, e.g., that all or asufficient portion of the source content has been mapped. The mappingprocess thus enables an operator to maximize effective mapping for agiven time that is available for mapping and allows an operator todefine a custom transformation “dictionary” that includes a minimizednumber of standardized terms that are defined relative to sourceelements in their native form.

According to another aspect of the present invention, contextualinformation is added to source content prior to transformation to assistin the transformation process. The associated method includes the stepsof: obtaining source information in a first form reflecting a firstsemantic environment; using a computer-based device to generateprocessed information that includes first content corresponding thesource information and second content, provided by the computer-baseddevice, regarding a context of a portion of the first content; andconverting the processed information into a second form reflecting asecond semantic environment.

The second content may be provided in the form of tags or other contextcues that serve to schematize the source information. For example, thesecond content may be useful in defining phrase boundaries, resolvinglinguistic ambiguities and/or defining family relationships betweensource chunks. The result is an information added input fortransformation that increases the accuracy and efficiency of thetransformation.

According to a further aspect of the present invention, an engine isprovided for transforming certain content of electronic transmissionsbetween semantic environments. First, a communication is established fortransmission between first and second user systems associated with firstand second semantic environments, respectively, and transmission of thecommunication is initiated. For example, a business form may beselected, filled out and addressed. The engine then receives thecommunication and, in substantially real-time, transforms the contentrelative to the source semantic environment, thereby providingtransformed content. Finally, the transmission is completed by conveyingthe transformed content between the user systems.

The engine may be embodied in a variety of different architectures. Forexample, the engine may be associated with the transmitting user systemrelative to the communication under consideration, the receiving usersystem, or at a remote site, e.g., a dedicated transformation gateway.Also, the transformed content may be fully transformed between the firstand second semantic environments by the engine, or may be transformedfrom one of the first and second semantic environments to anintermediate form, e.g., reflecting a standardized semantic environmentand/or neutral language. In the latter case, further manual and/orautomated processing may be performed in connection with the receivinguser system. In either case, such substantially real-time transformationof electronic content marks a significant step towards realizing theideal of globalization.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention and furtheradvantages thereof, reference is now made to the following detaileddescription taken in conjunction with the drawings, in which:

FIG. 1 is a monitor screen shot illustrating a process for developingreplacement rules in accordance with the present invention;

FIG. 2 is a monitor screen shot illustrating a process for developingordering rules in accordance with the present invention;

FIG. 3 is a schematic diagram of the NorTran Server components of a SOLxsystem in accordance with the present invention;

FIG. 4 is a flow chart providing an overview of SOLx systemconfiguration in accordance with the present invention.

FIGS. 5-10 are demonstrative monitor screen shots illustratingnormalization and translation processes in accordance with the presentinvention.

FIG. 11 is a flowchart of a normalization configuration process inaccordance with the present invention;

FIG. 12 is a flowchart of a translation configuration process inaccordance with the present invention;

FIG. 13 is an illustration of a graphical desktop implementation formonitoring the configuration process in accordance with the presentinvention;

FIG. 14 illustrates various network environment alternatives forimplementation of the present invention;

FIG. 15 illustrates a conventional network/web interface;

FIG. 16 illustrates a network interface for the SOLx system inaccordance with the present invention;

FIG. 17 illustrates a component level structure of the SOLx system inaccordance with the present invention; and

FIG. 18 illustrates a component diagram of an N-Gram Analyzer of theSOLx system in accordance with the present invention.

DETAILED DESCRIPTION

The present invention relates to a computer-based tool for facilitatingsubstantially real-time transformation of electronic communications. Asnoted above, the invention is useful in a variety of contexts, includingtransformation of business as well as non-business content and alsoincluding transformation of content across language boundaries as wellas within a single language environment. In the following description,the invention is described in connection with the transformation ofbusiness content from a source language to a target language using aStructured Object Localization expert (SOLx) system. This applicationserves to fully illustrate various aspects of the invention. It will beappreciated, however, that the invention is not limited to thisapplication.

In addition, in order to facilitate a more complete understanding of thepresent invention and its advantages over conventional machinetranslation systems, the following description includes considerablediscussion of grammar rules and other linguistic formalities. It shallbe appreciated that, to a significant degree, these formalities aredeveloped and implemented with the assistance of the SOLx system.Indeed, a primary advantage of the SOLx system is that it is intendedfor use by subject matter experts not linguistic experts. Moreover, theSOLx system can handle source data in its native form and does notrequire substantial database revision within the source system. The SOLxsystem thereby converts many service industry transformation tasks intotools that can be addressed by in-house personnel or substantiallyautomatically by the SOLx system.

The following description is generally divided into two sections. First,certain subjects relevant to the configuration of SOLx are described.This includes a discussion of configuration objectives as well as thenormalization and translation processes. Then, the structure of SOLx isdescribed, including a discussion of network environment alternatives aswell as the components involved in configuration and run-time operation.

A. System Configuration

1. Introduction—Configuration Challenges

The present invention addresses various shortcomings of manualtranslation and conventional machine translation, especially in thecontext of handling business content. In the former regard, the presentinvention is largely automated and is scalable to meet the needs of abroad variety of applications.

In the latter regard, there are a number of problems in typical businesscontent that interfere with good functioning of a conventional machinetranslation system. These include out-of-vocabulary (OOV) words that arenot really OOV and covert phrase boundaries. When a word to betranslated is not in the machine translation system's dictionary, thatword is said to be OOV. Often, words that actually are in the dictionaryin some form are not translated because they are not in the dictionaryin the same form in which they appear in the data under consideration.For example, particular data may contain many instances of the string“PRNTD CRCT BRD”, and the dictionary may contain the entry “PRINTEDCIRCUIT BOARD,” but since the machine translation system cannotrecognize that “PRNTD CRCT BRD” is a form of “PRINTED CIRCUIT BOARD”(even though this may be apparent to a human), the machine translationsystem fails to translate the term “PRNTD CRCT BRD”. The SOLx tool setof the present invention helps turn these “false OOV” terms into termsthat the machine translation system can recognize.

Conventional language processing systems also have trouble telling whichwords in a string of words are more closely connected than other sets ofwords. For example, humans reading a string of words like Acetic AcidGlass Bottle may have no trouble telling that there's no such thing as“acid glass,” or that the word Glass goes together with the word Bottleand describes the material from which the bottle is made. Languageprocessing systems typically have difficulty finding just such groupingsof words within a string of words. For example, a language processingsystem may analyze the string Acetic Acid Glass Bottle as follows:

i) Acetic and Acid go together to form a phrase

ii) Acetic Acid and Glass go together to form a phrase

iii) Acetic Acid Glass and Bottle go together to form a phrase

The first item of the analysis is correct, but the remaining two arenot, and they can lead to an incorrect analysis of the item descriptionas a whole. This faulty analysis may lead to an incorrect translation.

The actual boundaries between phrases in data are known as phraseboundaries. Phrase boundaries are often covert—that is, not visiblymarked. The SOLx tool of the present invention, as described in detailbelow, prepares data for translation by finding and marking phraseboundaries in the data. For example, it marks phrase boundaries in thestring Acetic Acid Glass Bottle as follows:

Acetic Acid|Glass Bottle

This simple processing step—simple for a human, difficult for a languageprocessing system—helps the machine translation system deduce thecorrect subgroupings of words within the input data, and allows it toproduce the proper translation.

The present invention is based, in part, on the recognition that somecontent, including business content, often is not easily searchable oranalyzable unless a schema is constructed to represent the content.There are a number of issues that a computational system must address todo this correctly. These include: deducing the “core” item; finding theattributes of the item; and finding the values of those attributes. Asnoted above, conventional language processing systems have troubletelling which words in a string of words are more closely connected thanother sets of words. They also have difficulty determining which word orwords in the string represent the “core,” or most central, concept inthe string. For example, humans reading a string of words like AceticAcid Glass Bottle in a catalogue of laboratory supplies may have notrouble telling that the item that is being sold is acetic acid, andthat Glass Bottle just describes the container in which it is packaged.For conventional language processing systems, this is not a simple task.As noted above, a conventional language processing system may identify anumber of possible word groupings, some of which are incorrect. Such alanguage processing system may deduce, for example, that the item thatis being sold is a bottle, and that the bottle is made of “acetic acidglass.” Obviously, this analysis leads to a faulty representation ofbottles (and of acetic acid) in a schema and, therefore, is of littleassistance in building an electronic catalogue system.

In addition to finding the “core” of an item description, it is alsouseful to find the groups of words that describe that item. In thefollowing description, the terms by which an item can be described aretermed its attributes, and the contents or quantity of an attribute istermed its value. Finding attributes and their values is as difficultfor a language processing system as is finding the “core” of an itemdescription. For instance, in the string Acetic Acid Glass Bottle, oneattribute of the core item is the package in which it is distributed.The value of this attribute is Glass Bottle. It may also be deemed thatone attribute of the core item is the kind of container in which it isdistributed. The value of this attribute would be Bottle. One canreadily imagine other container types, such as Drum, Bucket, etc., inwhich acetic acid could be distributed. It happens that the kind ofcontainer attribute itself has an attribute that describes the materialthat the container is made of. The value of this attribute is Glass.Conventional natural language processing systems have troubledetermining these sorts of relationships. Continuing with the exampleabove, a conventional language processing system may analyze the stringAcetic Acid Glass Bottle as follows:

Acetic and Acid go together to describe Glass

Acetic Acid and Glass go together to describe Bottle

This language processing system correctly deduced that Acetic and Acidgo together. It incorrectly concluded that Acetic Acid go together toform the value of some attribute that describes a kind of Glass, andalso incorrectly concluded that Acetic Acid Glass go together to givethe value of some attribute that describes the bottle in question.

The SOLx system of the present invention, as described in detail below,allows a user to provide guidance to its own natural language processingsystem in deducing which sets of words go together to describe values.It also adds one very important functionality that conventional naturallanguage processing systems cannot perform without human guidance. TheSOLx system allows you to guide it to match values with specificattribute types. The combination of (1) finding core items, and (2)finding attributes and their values, allows the SOLx system to builduseful schemas. As discussed above, covert phrase boundaries interferewith good translation. Schema deduction contributes to preparation ofdata for machine translation in a very straightforward way: the labelsthat are inserted at the boundaries between attributes corresponddirectly to phrase boundaries. The next section describes a number ofobjectives of the SOLx system configuration process. All of theseobjectives relate to manipulating data from its native from to a formmore amenable for translation or other localization, i.e., performing aninitial transformation to an intermediate form.

2. Configuration Objectives

Based on the foregoing, it will be appreciated that the SOLxconfiguration process has a number of objectives, including solving OOVsand solving covert phrase boundaries based on identification of coreitems and attribute/value pairs. Additional objectives, as discussedbelow, relate to taking advantage of reusable content chunks andresolving ambiguities. Many of these objectives are addressedautomatically, or are partially automated, by the various SOLx toolsdescribed below. The following discussion will facilitate a morecomplete understanding of the internal functionality of these tools asdescribed below.

False OOV words and true OOV words can be discovered at two stages inthe translation process: before translation, and after translation.Potential OOV words can be found before translation through use of aCandidate Search Engine as described in detail below. OOV words can beidentified after translation through analysis of the translated output.If a word appears in data under analysis in more than one form, theCandidate Search Engine considers the possibility that only one of thoseforms exists in the machine translation system's dictionary.Specifically, the Candidate Search Engine offers two ways to find wordsthat appear in more than one form prior to submitting data fortranslation: the full/abbreviated search option; and the case variantsearch option. Once words have been identified that appear in more thanone form, a SOLx operator can force them to appear in just one formthrough the use of vocabulary adjustment rules.

In this regard, the full/abbreviated search may output pairs ofabbreviations and words. Each pair represents a potential false OOV termwhere it is likely that the unabbreviated form is in-vocabulary.Alternatively, the full/abbreviated search may output both pairs ofwords and unpaired abbreviations. In this case, abbreviations that areoutput paired with an unabbreviated word are potentially false OOVwords, where the full form is likely in-vocabulary. Abbreviations thatare output without a corresponding full form may be true OOV words. Themachine translation dictionary may therefore be consulted to see if itincludes such abbreviations. Similarly, some entries in a machinetranslation dictionary may be case sensitive. To address this issue, theSOLx system may implement a case variant search that outputs pairs,triplets, etc. of forms that are composed of the same letters, butappear with different variations of case. The documentation for a givenmachine translation system can then be consulted to learn which casevariant is most likely to be in-vocabulary. To determine if a word isfalsely OOV, words that are suspected to be OOV can be compared with theset of words in the machine translation dictionary. There are threesteps to this procedure: 1) for each word that you suspect is falselyOOV, prepare a list of other forms that that word could take; 2) checkthe dictionary to see if it contains the suspected false OOV form; 3)check the dictionary to see if it contains one of the other forms of theword that you have identified. If the dictionary does not contain thesuspected false OOV word and does contain one of the other forms of theword, then that word is falsely OOV and the SOLx operator can force itto appear in the “in-vocabulary” form in the input data as discussedbelow. Generally, this is accomplished through the use of a vocabularyadjustment rule. The vocabulary adjustment rule converts the false OOVform to the in-vocabulary form. The process for writing such rules isdiscussed in detail below.

Problems related to covert phrase boundaries appear as problems oftranslation. Thus, a problem related to covert phrase boundaries mayinitially be recognized when a translator/translation evaluator findsrelated errors in the translated text. A useful objective, then, is toidentify these problems as problems related to covert phrase boundaries,rather than as problems with other sources. For example, a translationevaluator may describe problems related to covert phrase boundaries asproblems related to some word or words modifying the wrong word orwords. Problems related to potential covert phrase boundaries can alsobe identified via statistical analysis. As discussed below, the SOLxsystem includes a statistical tool called the N-gram analyzer (NGA) thatanalyzes databases to determine, among other things, what terms appearmost commonly and which terms appear in proximity to one another. Amistranslated phrase, identified in the quality control analysis(described below in relation to the TQE module), which has a low NGAprobability for the transition between two or more pairs of wordssuggests a covert phrase boundary. Problems related to covert phraseboundaries can also be addressed through modifying a schematicrepresentation of the data under analysis. In this regard, if a covertphrase boundary problem is identified, it is often a result of attributerules that failed to identify an attribute. This can be resolved bymodifying the schema to include an appropriate attribute rule. If aschema has no yet been produced for the data, a schema can beconstructed at this time. Once a categorization or attribute rule hasbeen constructed for a phrase that the translator/translation evaluatorhas identified as poorly translated, then the original text can bere-translated. If the result is a well-translated phrase, the problemhas been identified as one of a covert phrase boundary and the operatormay consider constructing more labeling rules for the data underanalysis. Covert phrase boundary problems can be addressed by building aschema, and then running the schematized data through a SOLx processthat inserts a phrase boundary at the location of every labeling/taggingrule.

The core item of a typical business content description is the item thatis being sold/described. An item description often consists of its coreitem and some terms that describe its various attributes. For example,in the item description Black and Decker ⅜″ drill with accessories, theitem that is being described is a drill. The words or phrases Black andDecker, ⅜″, and with accessories all give us additional informationabout the core item, but do not represent the core item itself. The coreitem in an item description can generally be found by answering thequestion, what is the item that is being sold or described here? Forexample, in the item description Black and Decker ⅜″ drill withaccessories, the item that is being described is a drill. The words orphrases Black and Decker, ⅜″, and with accessories all indicatesomething about the core item, but do not represent the core itemitself.

A subject matter expert (SME) configuring SOLx for a particularapplication can leverage his domain-specific knowledge before beginningyour work with SOLx by listing the attributes of core items beforebeginning work with SOLx, and by listing the values of attributes beforebeginning work with SOLx. Both classification rules and attribute rulescan then be prepared before manipulating data with the SOLx system.Domain-specific knowledge can also be leveraged by recognizing coreitems and attributes and their values during configuration of the SOLxsystem and writing rules for them as they appear. As the SME works withthe data within the SOLx system, he can write rules for the data as theneed appears. The Candidate Search Engine can also be used to perform acollocation search that outputs pairs of words that form collocations.If one of those words represents a core item, then the other word mayrepresent an attribute, a value, or (in some sense) both.Attribute-value pairs can also be identified based on a semanticcategory search implemented by the SOLx system. The semantic categorysearch outputs groups of item descriptions that share words belonging toa specific semantic category. Words from a specific semantic categorythat appear in similar item descriptions may represent a value, anattribute, or (in some sense) both.

Business content is generally characterized by a high degree ofstructure that facilitates writing phrasing rules and allows forefficient reuse of content “chunks.” As discussed above, much contentrelating to product descriptions and other structured content is notfree-flowing sentences, but is an abbreviated structure called a ‘nounphrase’. Noun phrases are typically composed of mixtures of nouns (N),adjectives (A), and occasionally prepositions (P). The mixtures of nounsand adjectives may be nested. The following are some simple examples:

TABLE 1 A N Ceramic insulator N N Distribution panel A A N Largemetallic object A N N Variable speed drill N A N Plastic coated plate NN N Nine pin connector N P N Angle of entryAdjective phrases also exist mixed with adverbs (Av). Table 2 lists someexamples.

TABLE 2 Av A Manually operable N A Color coded N N A Carbon fiberreinforcedThe noun phrase four-strand color-coded twisted-pair telephone wire hasthe pattern NNNAANNN. It is grouped as (four_(N) strand_(N))_(N)(color_(N) coded_(A))_(A) (twisted_(A) pair_(N))_(N) telephone_(N)wire_(N). Another way to look at this item is an object-attribute list.The primary word or object is wire; of use type telephone; strand typetwisted-pair, color property color-coded, and strand number type isfour-stranded. The structure is N₁AN₂N₃N₄. With this type of compoundgrouping, each group is essentially independent of any other group.Hence, the translation within each group is performed as an independentphrase and then linked by relatively simple linguistic rules.

For example, regroup N₁AN₂N₃N₄ as NN₃N₄ where N=N₁AN₂. In Spanish thiscan be translated as NN₃N₄→N₄‘de’ N₃ ‘de’ {N} where {N} means thetranslated version of N, and → means translated as. In Spanish, it wouldbe N₁AN₂→N₂A ‘de’ N₁. The phrase then translates as N₁AN₂N₃N₄ →N₄ ‘de’N₃ ‘de’ N₂ A ‘de’ N₁.

In addition to defining simple rule sets for associating translatedcomponents of noun phrases, there is another factor that leads to thefeasibility of automatically translating large component databases. Thisadditional observation is that very few terms are used in creating thesedatabases. For example, databases have been analyzed that have 70,000part descriptions, yet are made up of only 4,000 words or tokens.Further, individual phrases are used hundreds of times. In other words,if the individual component pieces or “chunks” are translated, and thereare simple rules for relating theses chunks, then the translation oflarge parts of the content, in principle, is straightforward. The SOLxsystem includes tools as discussed in more detail below for identifyingreusable chunks, developing rules for translation and storing translatedterms/chunks for facilitating substantially real-time transformation ofelectronic content.

Another objective of the configuration process is enabling SOLx toresolve certain ambiguities. Ambiguity exists when a language processingsystem does not know which of two or more possible analyses of a textstring is the correct one. There are two kinds of ambiguity in itemdescriptions: lexical ambiguity and structural ambiguity. When properlyconfigured, the SOLx system can often resolve both kinds of ambiguity.

Lexical ambiguity occurs when a language processing system does not knowwhich of two or more meanings to assign to a word. For example, theabbreviation mil can have many meanings, including million, millimeter,military, and Milwaukee. In a million-item database of tools andconstruction materials, it may occur with all four meanings. Intranslation, lexical ambiguity leads to the problem of the wrong wordbeing used to translate a word in your input. To translate yourmaterial, it is useful to expand the abbreviation to each of itsdifferent full forms in the appropriate contexts. The user can enablethe SOLx system to do this by writing labeling rules that distinguishthe different contexts from each other. For example, mil might appearwith the meaning million in the context of a weight, with the meaningmillimeter in the context of a length, with the meaning military in thecontext of a specification type (as in the phrase MIL SPEC), and withthe meaning Milwaukee in the context of brand of a tool. You then writevocabulary adjustment rules to convert the string mil into theappropriate full form in each individual context. In schematization,resolving lexical ambiguity involves a number of issues, includingidentification of the core item in an item description; identificationof values for attributes; and assignment of values to proper attributes.

Structural ambiguity occurs when a language processing system does notknow which of two or more labeling rules to use to group together setsof words within an item description. This most commonly affectsattribute rules and may require further nesting of parent/child tagrelationships for proper resolution.

3. Configuration Processes

a. Normalization

As the foregoing discussion suggests, the various configurationobjectives (e.g., resolving false OOVs, identifying covert phraseboundaries, taking advantage of reusable chunks and resolvingambiguities) can be addressed in accordance with the present inventionby transforming input data from its native form into an intermediateform that is more amenable to translation or otherlocalization/transformation. The corresponding process, which is aprimary purpose of SOLx system configuration, is termed “normalization.”Once normalized, the data will include standardized terminology in placeof idiosyncratic terms, will reflect various grammar and other rulesthat assist in further processing, and will include tags that providecontext for resolving ambiguities and otherwise promoting propertransformation. The associated processes are executed using theNormalization Workbench of the SOLx system, as will be described below.There are two kinds of rules developed using the NormalizationWorkbench: grammatical rules, and normalization rules. The purpose of agrammatical rule is to group together and label a section of text. Thepurpose of a normalization rule is to cause a labeled section of text toundergo some change. Although these rules are discussed in detail belowin order to provide a more complete understanding of the presentinvention, it will be appreciated that these rules are, to a largeextent, developed and implemented internally by the various SOLx tools.Accordingly, SOLx operators need not have linguistics expertise torealize the associated advantages.

i) Normalization Rules

The Normalization Workbench offers a number different kinds ofnormalization rules relating to terminology including: replacementrules, joining rules, and ordering rules. Replacement rules allow thereplacement of one kind of text with another kind of text. Differentkinds of replacement rules allow the user to control the level ofspecificity of these replacements. Joining rules allow the user tospecify how separated elements should be joined together in the finaloutput. Ordering rules allow the user to specify how different parts ofa description should be ordered relative to each other.

With regard to replacement rules, data might contain instances of theword centimeter written four different ways—as cm, as cm., as c.m., andas centimeter—and the user might want to ensure that it always appearsas centimeter. The Normalization Workbench implements two differentkinds of replacement rules: unguided replacement, and guidedreplacement. The rule type that is most easily applicable to aparticular environment can be selected. Unguided replacement rules allowthe user to name a tag/category type, and specify a text string to beused to replace any text that is under that tag. Guided replacementrules allow the user to name a tag/category type, and specify specifictext strings to be used to replace specific text strings that are underthat tag. Within the Normalization Workbench logic, the format ofunguided replacement rules may be, for example:

[category_type]→‘what to replace its text with’

For instance, the following rule says to find any [foot] category label,and replace the text that it tags with the word feet:

[foot]→‘feet’

If that rule was run against the following input,

Steel piping 6 [foot] foot long

Steel piping 3 [foot] feet long

it would produce the following output:

Steel piping 6 [foot] feet long

Steel piping 3 [foot] feet long

The second line is unchanged; in the first line, foot has been changedto feet.

Guided replacement rules allow the user to name a tag/category type, andspecify specific text strings to be used to replace specific textstrings that are under that tag. This is done by listing a set ofpossible content strings in which the normalization engine should “lookup” the appropriate replacement. The format of these rules is:

[category_type] :: lookup  ‘text to replace’ => ‘text to replace itwith’  ‘other text to replace’ => ‘text to replace it with’  ‘more textto replace’ => ‘text to replace it with’ end lookupFor instance, the following rule says to find any [length_metric] label.If you see mm, mm., m.m., or m. m. beneath it, then replace it withmillimeter. If you see cm, cm., c.m., or c. m. beneath it, then replaceit with centimeter:

[length_metric] :: lookup  ‘mm’ => ‘millimeter’  ‘mm.’ => ‘millimeter’ ‘m.m.’ => ‘millimeter’  ‘m. m.’ => ‘millimeter’  ‘cm’ => ‘centimeter’ ‘cm.’ => ‘centimeter’  ‘c.m.’ => ‘centimeter’   ‘c. m.’ => ‘centimeter’end lookupIf that rule was run against the following inputStainless steel scalpel handle, [length_metric] (5 mm)[length_metric] (5 mm) disposable plastic scalpel handleit would produce the following output:Stainless steel scalpel handle, [length_metric] (5 millimeter)[length_-metric] (5 millimeter) disposable plastic scalpel handle

From the user's perspective, such replacement rules may be implementedvia a simple user interface such as shown in FIG. 1. FIG. 1 shows a userinterface screen 100 including a left pane 102 and a right pane 104. Theleft pane 102 displays the grammar rules that are currently in use. Therules are shown graphically, including alternative expressions (in thiscase) as well as rule relationships and categories. Many alternativeexpressions or candidates therefor are automatically recognized by theworkbench and presented to the user. The right pane 104 reflects theprocess to update or add a text replacement rule. In operation, agrammar rule is selected in the left pane 102. All text that canrecognized by the rule appears in the left column of the table 106 inthe right pane 104. The SME then has the option to unconditionallyreplace all text with the string from the right column of the table 106or may conditionally enter a replacement string. Although not shown ineach case below, similar interfaces allow for easy development andimplementation of the various rules discussed herein.

Joining rules allow the user to specify how separated elements should bejoined together in the final output. Joining rules can be used tore-join elements that were separated during the process of assigningcategory labels. The user can also use joining rules to combine separateelements to form single delimited fields.

Some elements that were originally adjacent in the input may have becomeseparated in the process of assigning them category labels, and it maybe desired to re-join them in the output. For example, the catheter tipconfiguration JL4 will appear as [catheter_tip_configuration] (J L 4)after its category label is assigned. However, the customary way towrite this configuration is with all three of its elements adjacent toeach other. Joining rules allow the user to join them together again.

The user may wish the members of a particular category to form a single,delimited field. For instance, you might want the contents of thecategory label [litter_box] (plastic hi-impact scratch-resistant) toappear as plastic,hi-impact,scratch-resistant in order to conserve spacein your data description field. Joining rules allow the user to jointhese elements together and to specify that a comma be used as thedelimiting symbol.

The format of these rules is:

[category_label]:: join with ‘delimiter’

The delimiter can be absent, in which case the elements are joinedimmediately adjacent to each other. For example, numbers emerge from thecategory labeler with spaces between them, so that the number twelvelooks like this:

[real] (12)

A standard normalization rule file supplied with the NormalizationWorkbench contains the following joining rule:

[real]:: join with″

This rule causes the numbers to be joined to each other without anintervening space, producing the following output:

[real] (12)

The following rule states that any content that appears with thecategory label [litter_box] should be joined together with commas:

[litter_box]:: join with ‘,’

If that rule was run against the following input,

[litter_box] (plastic hi-impact dog-repellant)

[litter_box] (enamel shatter-resistant)

it would produce the following output:

[litter_box] (plastic,hi-impact,dog-repellant)

[litter_box] (enamel,shatter-resistant)

Ordering rules allow the user to specify how different parts of adescription should be ordered relative to each other. For instance,input data might contain catheter descriptions that always contain acatheter size and a catheter type, but in varying orders-sometimes withthe catheter size before the catheter type, and sometimes with thecatheter type before the catheter size:

[catheter] ([catheter_size] (8Fr) [catheter_type] (JL4) [item](catheter))

[catheter] ([catheter_type] (JL5) [catheter_size] (8Fr) [item](catheter))

The user might prefer that these always occur in a consistent order,with the catheter size coming first and the catheter type coming second.Ordering rules allow you to enforce this ordering consistently.

The internal format of ordering rules is generally somewhat morecomplicated than that of the other types of rules. Ordering rulesgenerally have three parts. Beginning with a simple example:

[catheter]/[catheter_type] [catheter_size]→($2 $1)

The first part of the rule, shown in bold below, specifies that thisrule should only be applied to the contents of a [catheter] categorylabel:

[catheter]/[catheter_type] [catheter_size]→($2 $1)

The second part of the rule, shown in bold below, specifies whichlabeled elements are to have their orders changed:

[catheter]/[catheter_type] [catheter_size]→($2 $1)

Each of those elements is assigned a number, which is written in theformat $number in the third part of the rule. The third part of therule, shown in bold below, specifies the order in which those elementsshould appear in the output:

[catheter]/[catheter_type] [catheter_size]→($2 $1)

The order $2 $1 indicates that the element which was originally second(i.e., $2) should be first (since it appears in the leftmost position inthe third part of the rule), while the element which was originallyfirst (i.e., $1) should be second (since it appears in the secondposition from the left in the third part of the rule). Ordering rulescan appear with any number of elements. For example, this rule refers toa category label that contains four elements. The rule switches theposition of the first and third elements of its input, while keeping itssecond and fourth elements in their original positions:[resistor]/[resistance] [tolerance] [wattage] [manufacturer]→($3 $2 $1$4)

FIG. 2 shows an example of a user interface screen 200 that may be usedto develop and implement an ordering rule. The screen 200 includes aleft pane 202 and a right pane 204. The left pane 202 displays thegrammar rules that are currently in use—in this case, ordering rules forcontainer size—as well as various structural productions under eachrule. The right pane 204 reflects the process to update or addstructural reorganization to the rule. In operation, a structural ruleis selected using the left pane 202. The right pane 204 can then be usedto develop or modify the rule. In this case, the elements or “nodes” canbe reordered by simple drag-and-drop process. Nodes may also be added ordeleted using simple mouse or keypad commands.

Ordering rules are very powerful, and have other uses besidesorder-changing per se. Other uses for ordering rules include thedeletion of unwanted material, and the addition of desired material.

To use an ordering rule to delete material, the undesired material canbe omitted from the third part of the rule. For example, the followingrule causes the deletion of the second element from the productdescription:

[notebook]/[item] [academic_field] [purpose]→($1 $3)

If that rule was run against the following input,

[notebook] ([item] (notebook) [academic_field] (linguistics) [purpose](fieldwork)

[notebook] ([item] (notebook) [academic_field] (sociology) [purpose](fieldwork)

it would produce the following output:

[notebook] ([item] (notebook) [purpose] (fieldwork)

[notebook] ([item] (notebook) [purpose] (fieldwork)

To use an ordering rule to add desired material, the desired materialcan be added to the third part of the rule in the desired positionrelative to the other elements. For example, the following rule causesthe string [real_cnx]‘−’ to be added to the product description:

[real]/[integer] [fraction])→($1 [real_cnx]‘−’ $2)

If that rule was run against the following input,

[real] ( 11/2)

[real] ( 15/8)

it would produce the following output:

[real] (1 [real_cnx] (−) ½)

[real] (1 [real_cnx] (−) ⅝)

After final processing, this converts the confusing 11/2 and 15/8 to 1½(“one and a half”) and 1⅝ (“one and five eighths”).

In addition to the foregoing normalization rules relating toterminology, the SOLx system also involves normalization rules relatingto context cues and phrasing. The rules that the SOLx system uses toidentify contexts and determine the location and boundaries ofattribute/value pairs fall into three categories: categorization rules,attribute rules, and analysis rules. Categorization rules and attributerules together form a class of rules known as labeling/tagging ruleslabeling/tagging rules cause the insertion of labels/tags in the outputtext when the user requests parsed or labeled/tagged texts. They formthe structure of the schema in a schematization task, and they becomephrase boundaries in a machine translation task. Analysis rules do notcause the insertion of labels/tags in the output. They are insertedtemporarily by the SOLx system during the processing of input, and aredeleted from the output before it is displayed.

Although analysis tags are not displayed in the output (SOLx can allowthe user to view them if the data is processed in a defined interactivemode), they are very important to the process of determining contextsfor vocabulary adjustment rules and for determining where labels/tagsshould be inserted. The analysis process is discussed in more detailbelow.

ii. Grammar Rules

The various rules described above for establishing normalized contentare based on grammar rules developed for a particular application. Theprocess for developing grammar rules is set forth in the followingdiscussion. Again, it will be appreciated that the SOLx tools guide anSME through the development of these rules and the SME need not have anyexpertise in this regard. There are generally two approaches to writinggrammar rules, known as “bottom up” and “top down.” Bottom-up approachesto writing grammar rules begin by looking for the smallest identifiableunits in the text and proceed by building up to larger units made up ofcohesive sets of the smaller units. Top-down approaches to writinggrammar rules begin by identifying the largest units in the text, andproceed by identifying the smaller cohesive units of which they aremade.

Consider the following data for an example of building grammar rulesfrom the bottom up. It consists of typical descriptions of variouscatheters used in invasive cardiology:

8Fr. JR4 Cordis

8 Fr. JR5 Cordis

8Fr JL4 catheter, Cordis, 6/box

8Fr pigtail 6/box

8 French pigtail catheter, 135 degree

8Fr Sones catheter, reusable

4Fr. LC angioplasty catheter with guidewire and peelaway sheath

Each of these descriptions includes some indication of the (diametric)size of the catheter, shown in bold text below:

8Fr. JR4 Cordis

8 Fr. JR5 Cordis

8Fr JL4 catheter, Cordis, 6/box

8Fr pigtail 6/box

8 French pigtail catheter, 135 degree

8Fr Sones catheter, reusable

4Fr. LC angioplasty catheter with guidewire and peelaway sheath

One can make two very broad generalizations about these indications ofcatheter size: all of them include a digit, and the digits all seem tobe integers.

One can further make two weaker generalizations about these indicationsof catheter size: all of them include either the letters Fr, or the wordFrench; and if they include the letters Fr, those two letters may or maynot be followed by a period.

A subject matter expert (SME) operating the SOLx system will know thatFr, Fr., and French are all tokens of the same thing: some indicator ofthe unit of catheter size. Having noted these various forms in the data,a first rule can be written. It will take the form x can appear as w, y,or z, and this rule will describe the different ways that x can appearin the data under analysis.

The basic fact that the rule is intended to capture is French can appearas Fr, as Fr., or as French.

In the grammar rules formalism, that fact may be indicated like this:

[French]

(Fr)

(Fr.)

(French)

[French] is the name assigned to the category of “things that can beforms of the word that expresses the unit of size of catheters” andcould just as well have been called [catheter_size_unit], or [Fr], or[french]. The important thing is to give the category a label that ismeaningful to the user.(Fr), (Fr.), and (French) are the forms that a thing that belongs to thecategory [French] can take. Although the exact name for the category[French] is not important, it matters much more how these “rulecontents” are written. For example, the forms may be case sensitive.That is, (Fr) and (fr) are different forms. If your rule contains theform (Fr), but not the form (fr), then if there is a description likethis:8 fr cordis catheterThe fr in the description will not be recognized as expressing a unit ofcatheter size. Similarly, if your rule contained the form (fr), but notthe form (Fr), then Fr would not be recognized. “Upper-case” and“lower-case” distinctions may also matter in this part of a rule.

Returning to the list of descriptions above, a third generalization canbe made: all of the indications of catheter size include an integerfollowed by the unit of catheter size.

This suggests another rule, of the form all x consist of the sequence afollowed by b. The basic fact that the rule is intended to capture is:all indications of catheter size consist of a number followed by someform of the category [French].

In the grammar rules formalism, that fact may be indicated like this:

>[catheter_size]  ([real][French])[catheter_size] is the name assigned to the category of “groups of wordsthat can indicate the size of a catheter;” and could just as well havebeen called [size], or [catheterSize], or [sizeOfACatheter]. Theimportant thing is to give the category a label that is meaningful tothe user.([real] [French]) is the part of the rule that describes the things thatmake up a [catheter_size]—that is, something that belongs to thecategory of things that can be [French], and something that belongs tothe categories of things that can be [real]—and what order they have toappear in—in this case, the [real] first, followed by the [French]. Inthis part of the rule, exactly how things are written is important.In this rule, the user is able to make use of the rule for [French] thatwas defined earlier. Similarly, the user is able to make use of the[real] rule for numbers that can generally be supplied as a standardrule with the Normalization Workbench. Rules can make reference to otherrules. Furthermore, rules do not have to be defined in the same file tobe used together, as long as the parser reads in the file in which theyare defined.

So far this example has involved a set of rules that allows descriptionof the size of every catheter in a list of descriptions. The SME workingwith this data might then want to write a set of rules for describingthe various catheter types in the list. Up to this point, this examplehas started with the smallest units of text that could be identified(the different forms of [French]) and worked up from there (to the[catheter_size] category). Now, the SME may have an idea of ahigher-level description (i.e., catheter type), but no lower-leveldescriptions to build it up out of; in this case, the SME may start atthe top, and think his way down through a set of rules.

The SME can see that each of these descriptions includes some indicationof the type of the catheter, shown in bold text below:

8Fr. JR4 Cordis

8 Fr. JR5 Cordis

8Fr JL4 catheter, Cordis, 6/box

8Fr pigtail 6/box

8 French pigtail catheter, 135 degree

8Fr Sones catheter, reusable

4Fr. angioplasty catheter with guidewire and peelaway sheath

He is aware that a catheter type can be described in one of two ways: bythe tip configuration of the catheter, and by the purpose of thecatheter. So, the SME may write a rule that captures the fact thatcatheter types can be identified by tip configuration or by catheterpurpose.In the grammar rules formalism, that fact may be indicated like this:

>[catheter_type]  ([catheter_tip_configuration])  ([catheter_purpose])This involves a rule for describing tip configuration, and a rule foridentifying a catheter's purpose.

Starting with tip configuration, the SME knows that catheter tipconfigurations can be described in two ways: 1) by a combination of theinventor's name, an indication of which blood vessel the catheter ismeant to engage, and by an indication of the length of the curve at thecatheter tip; or 2) by the inventor's name alone.

The SME can write a rule that indicates these two possibilities in thisway:

[catheter_tip_configuration]  ([inventor][coronary_artery][curve_size]) ([inventor])In this rule, [catheter_tip_configuration] is the category label;([inventor] [coronary_artery] [curve_size]) and ([inventor]) are the twoforms that things that belong to this category can take. In order to usethese rules, the SME will need to write rules for [inventor],[coronary_artery], and [curve_size]. The SME knows that in all of thesecases, the possible forms that something that belongs to one of thesecategories can take are very limited, and can be listed, similarly tothe various forms of [French]:[inventor]

(J)

(Sones)

[coronary-artery]

(L)

(R)

[curve_size]

(3.5)

(4)

(5)

With these rules, the SME has a complete description of the[catheter_tip_configuration] category. Recall that the SME is writing a[catheter_tip_configuration] rule because there are two ways that acatheter type can be identified: by the configuration of the catheter'stip, and by the catheter's purpose. The SME has the[catheter_tip_configuration] rule written now and just needs a rule thatcaptures descriptions of a catheter's purpose.

The SME is aware that (at least in this limited data set) a catheter'spurpose can be directly indicated, e.g. by the word angioplasty, or canbe inferred from something else—in this case, the catheter's shape, asin pigtail. So, the SME writes a rule that captures the fact thatcatheter purpose can be identified by purpose indicators or by cathetershape.

In the grammar rules formalism, that fact can be indicated like this:

[catheter_purpose]  ([catheter_purpose_indicator])  ([catheter_shape])The SME needs a rule for describing catheter purpose, and a rule fordescribing catheter shape. Both of these can be simple in this example:

[catheter_purpose_indicator]  (angioplasty) [catheter_shape]  (pigtail)With this, a complete set of rules is provided for describing cathetertype, from the “top” (i.e., the [catheter_type] rule) “down” (i.e., tothe rules for [inventor], [coronary_artery], [curve_size],[catheter_purpose], and [catheter_shape]).

“Top-down” and “bottom-up” approaches to writing grammar rules are botheffective, and an SME should use whichever is most comfortable orefficient for a particular data set. The bottom-up approach is generallyeasier to troubleshoot; the top-down approach is more intuitive for somepeople. A grammar writer can use some combination of both approachessimultaneously.

Grammar rules include a special type of rule called a wanker. Wankersare rules for category labels that should appear in the output of thetoken normalization process. In one implementation, wankers are writtensimilarly to other rules, except that their category label starts withthe symbol >. For example, in the preceding discussion, we wrote thefollowing wanker rules:

>[catheter_size]  ([real][French]) >[catheter_type] ([catheter_tip_configuration])  ([catheter_purpose])Other rules do not have this symbol preceding the category label, andare not wankers.

Chunks of text that have been described by a wanker rule will be taggedin the output of the token normalization process. For example, with therule set that we have defined so far, including the two wankers, wewould see output like the following:

[catheter_size] (8Fr.) [catheter_type] (JR4) Cordis

[catheter_size] (8 Fr.) [catheter_type] (JR5) Cordis

[catheter size] (8Fr) [catheter_type] (JL4) catheter, Cordis, 6/box

[catheter_size] (8Fr) [catheter_type] (pigtail) 6/box

[catheter_size] (8 French) [catheter_type] (pigtail) catheter, 135degree

[catheter_size](8Fr) [catheter_type] (Sones) catheter, reusable

[catheter size] (4Fr.) LC [catheter_type] (angioplasty) catheter withguidewire and peelaway sheath

Although the other rules are used in this example to define the wankerrules, and to recognize their various forms in the input text, since theother rules are not wankers, their category labels do not appear in theoutput. If at some point it is desired to make one or more of thoseother rules' category labels to appear in the output, the SME or otheroperator can cause them to do so by converting those rules to wankers.

Besides category labels, the foregoing example included two kinds ofthings in rules. First, the example included rules that contained othercategory labels. These “other” category labels are identifiable in theexample by the fact that they are always enclosed in square brackets,e.g.

[catheter_purpose]  ([catheter_purpose_indicator])  ([catheter_shape])

The example also included rules that contained strings of text that hadto be written exactly the way that they would appear in the input. Thesestrings are identifiable by the fact that they are directly enclosed byparentheses, e.g.

[French]

(Fr)

(Fr.)

(French)

There is a third kind of thing that can be used in a rule. These things,called regular expressions, allow the user to specify approximately whata description will look like. Regular expressions can be recognized bythe facts that: unlike the other kinds of rule contents, they are notenclosed by parentheses, and they are immediately enclosed by “forwardslashes.”

Regular expressions in rules look like this:

[angiography_catheter_french_size]  /7|8/ [rocket_engine_size] /{circumflex over ( )}X\d{2}/ [naval_vessel_hull_number]  /\w+\d+/

Although the foregoing example illustrated specific implementations ofspecific rules, it will be appreciated that a virtually endless varietyof specialized rules may be provided in accordance with the presentinvention. The SOLx system of the present invention consists of manycomponents, as will be described below. One of these components is theNatural Language Engine module, or NLE. The NLE module evaluates eachitem description in data under analysis by means of rules that describethe ways in which core items and their attributes can appear in thedata. The exact (machine-readable) format that these rules take can varydepending upon the application involved and computing environment. Forpresent purposes, it is sufficient to realize that these rules expressrelationships like the following (stated in relation to the drillexample discussed above):

-   -   Descriptions of a drill include the manufacturer's name, the        drill size, and may also include a list of accessories and        whether or not it is battery powered.    -   A drill's size may be three eighths of an inch or one half inch    -   inch may be written as inch or as ″    -   If inch is written as ″, then it may be written with or without        a space between the numbers ⅜ or ½ and the ″

The NLE checks each line of the data individually to see if any of therules seem to apply to that line. If a rule seems to apply, then the NLEinserts a label/tag and marks which string of words that rule seemed toapply to. For example, for the set of rules listed above, then in theitem description Black and Decker ⅜″ drill with accessories, the NLEmodule would notice that ⅜″ might be a drill size, and would mark it assuch. If the user is running the NLE in interactive mode, he may observesomething like this in the output:

[drill_size] (⅜″)

In addition to the rules listed above, a complete set of rules fordescribing the ways that item descriptions for/of drills and theirattributes would also include rules for manufacturers' names, accessorylists, and whether or not the drill is battery powered. If the userwrites such a set of rules, then in the item description Black andDecker ⅜″ drill with accessories, the NLE module will notice andlabel/tag the following attributes of the description:

[manufacturer_name] (Black and Decker)

[drill_size] (⅜″)

[

The performance of the rules can be analyzed in two stages. First,determine whether or not the rules operate adequately. Second, if it isidentified that rules that do not operate adequately, determine why theydo not operate adequately.

For translations, the performance of the rules can be determined byevaluating the adequacy of the translations in the output text. Forschematization, the performance of the rules can be determined byevaluating the adequacy of the schema that is suggested by running therule set. For any rule type, if a rule has been identified that does notperform adequately, it can be determined why it does not operateadequately by operating the NLE component in interactive mode withoutput to the screen.

For tagging rules, test data set can be analyzed to determine if: everyitem that should be labeled/tagged has been labeled/tagged and any itemthat should not have been labeled/tagged has been labeled/tagged inerror.

In order to evaluate the rules in this way, the test data set mustinclude both items that should be labeled/tagged, and items that shouldnot be tagged.

Vocabulary adjustment rules operate on data that has been processed bytagging/tagging rules, so troubleshooting the performance of vocabularyadjustment rules requires attention to the operation of tagging/taggingrules, as well as to the operation of the vocabulary adjustment rulesthemselves.

In general, the data set selected to evaluate the performance of therules should include: examples of different types of core items, and foreach type of core item, examples with different sets of attributesand/or attribute values.

b. Translation

The SOLx paradigm is to use translators to translate repeatable complexterms and phrases, and translation rules to link these phrases together.It uses the best of both manual and machine translation. The SOLx systemuses computer technology for repetitive or straightforward applications,and uses people for the complex or special-case situations. The NorTran(Normalization/Translation) server is designed to support this paradigm.FIG. 3 represents a high-level architecture of the NorTran platform 300.Each module is discussed below as it relates to thenormalization/translation process. A more detailed description isprovided below in connection with the overall SOLx schematic diagramdescription for configuration and run-time operation.

The GUI 302 is the interface between the subject matter expert (SME) orhuman translator (HT) and the core modules of the NorTran server.Through this interface, SMEs and HTs define the filters for contentchunking, access dictionaries, create the terms and phrasesdictionaries, and monitor and edit the translated content.

This N-Gram 304 filter for the N-gram analysis defines the parametersused in the N-gram program. The N-gram program is the key statisticaltool for identifying the key reoccurring terms and phrases of theoriginal content.

The N-Gram and other statistical tools module 306 is a set of parsingand statistical tools that analyze the original content for significantterms and phrases. The tools parse for the importance of two or morewords or tokens as defined by the filter settings. The output is asorted list of terms with the estimated probabilities of the importanceof the term in the totality of the content. The goal is to aggregate thelargest re-usable chunks and have them directly translated.

The chunking assembly and grammar rules set 308 relates the pieces fromone language to another. For example, as discussed earlier, two nounphrases N₁N₂ are mapped in Spanish as N₂‘de’ N₁. Rules may need to beadded or existing ones modified by the translator. The rules are used bythe translation engine with the dictionaries and the original content(or the normalized content) to reassemble the content in its translatedform.

The rules/grammar base language pairs and translation engine 310constitute a somewhat specialized machine translation (MT) system. Thetranslation engine portion of this system may utilize any of variouscommercially available translation tools with appropriate configurationof its dictionaries.

Given that the translation process is not an exact science and thatround trip processes (translations from A to B to A) rarely work, astatistical evaluation is likely the best automatic tool to assess theacceptability of the translations. The Translation Accuracy Analyzer 312assesses words not translated, heuristics for similar content, baselineanalysis from human translation and other criteria.

The chunking and translation editor 314 functions much like atranslator's workbench. This tool has access to the original content;helps the SME create normalized content if required; the normalizedcontent and dictionaries help the translator create the translated termsand phase dictionary, and when that repository is created, helps thetranslator fill in any missing terms in the translation of the originalcontent. A representation of the chunking functionality of this editoris shown in the example in Table 3.

TABLE 3 Chunk Chunked Orig Original Content Normalized Terms Freq No.Cont Round Baker (A) Poland Emile Henry 6 1 7-A-6 Round Baker withHandles (B) Poland Oval Baker 6 2 7-18-B-6 Oval Baker (C) Red E. HenryLasagna Baker 4 3 2-C-15-1 Oval Baker (D) Polish Pottery Polish Pottery4 4 2-D-5 Oval Baker (E) Red, Emile Henry Poland 2 5 2-E-15-1 Oval Baker(F) Polish Pottery Round Baker 2 6 2-F-5 Oval Baker (G) Polish PotteryBaker Chicken Shaped 1 7 2-G-5 Oval Baker Polish Pottery (H) Baker DeepDish SIGNITURE 1 8 2--5-H Lasagna Baker (I) Emile Henry Cobalt Bakerwith cover/handles 1 9 4-I-1-13 Lasagna Baker (I) Emile Henry GreenBaker Rectangular 1 10 4-I-1-14 Lasagna Baker (I) Emile Henry RedCeramic 1 11 4-I-1-15 Lasagna Baker (I) Emile Henry Yellow cobalt 1 124-I-1-17 Baker Chicken Shaped (J) green 1 13 8-J Baker Deep DishSIGNATURE (K) red 1 14 9-K Baker Rectangular (L) White Ceramic Signature1 15 11-L-18-12 Baker with cover/handles Polish Pottery yellow 1 16 10-5white 1 17 with Handles 1 18

The first column lists the original content from a parts list of cookingdishes. The term (A) etc. are dimensional measurements that are notrelevant to the discussion. The second column lists the chunked termsfrom an N-gram

TABLE 4 Normalized Terms Spanish Translation Emile Henry Emile HenryOval Baker Molde de Hornear Ovalado Lasagna Baker Molde de Hornear paraLasagna Polish Pottery Alfarería Polaca Poland Polonia (if Country),Poland (if brandname) Round Baker Molde de Hornear Redondo BakerChicken- Molde de Hornear en Forma de Pollo Shaped Baker Deep Dish Moldede Hornear Plato Profundo SIGNATURE SIGNITURE Baker with Molde deHornear con Tapa/Asas cover/handles Baker Rectangular Molde de HornearRectangular Ceramic Alfarería cobalt Cobalto green Verde red RojoSignature SIGNATURE (brandname) FIRMA (not brand name) yellow Amarillowhite Blanco with Handles Con Asasanalysis; the third column lists the frequency of each term in theoriginal content set. The fourth column is the number associated withthe chunk terms in column 2. The fifth column is the representation ofthe first column in terms of the sequence of chunked content.

If the translation of each chunk is stored in another column, andtranslation rules exist for reassembling the chunks, then the content istranslated. It could be listed in another column that would have adirect match or link to the original content. Table 4 lists thenormalized and translated normalized content.

Finally, Table 5 shows the Original Content and the Translated Contentthat is created by assembling the Translated Normalized Terms in Table 4according to the Chunked Original Content sequence in Table 3.

TABLE 5 Original Content Translated Content Round Baker (A) Poland Moldede Hornear Redondo (A) Polonia Round Baker with Handles Molde de HornearRedondo Con Asas (B) (B) Poland Polonia Oval Baker (C) Red Emile Moldede Hornear Ovalado Rojo Emile Henry Henry Oval Baker (D) Polish PotteryMolde de Hornear Ovalado (D) Alfarería Polaca Oval Baker (E) Red, EmileMolde de Hornear Ovalado (E) Rojo, Henry Emile Henry Oval Baker (F)Polish Pottery Molde de Hornear Ovalado (F) Alfarería Polaca Oval Baker(G) Polish Pottery Molde de Hornear Ovalado (G) Alfarería Polaca OvalBaker Polish Pottery (H) Molde de Hornear Ovalado Alfarería Polaca (H)Lasagna Baker (I) Emile Molde de Hornear para Lasagna (I) Emile HenryCobalt Henry Cobalto Lasagna Baker (I) Emile Molde de Hornear paraLasagna (I) Emile Henry Green Henry Verde Lasagna Baker (I) Emile Moldede Hornear para Lasagna (I) Emile Henry Red Henry Rojo Lasagna Baker (I)Emile Molde de Hornear para Lasagna (I) Emile Henry Yellow HenryAmarillo Baker Chicken Shaped (J) Molde de Hornear en Forma de Pollo (J)Baker Deep Dish Molde de Hornear Plato Profundo SIGNATURE (K) SIGNATURE(K) Baker Rectangular (L) White Molde de Hornear Rectangular (L) BlancoCeramic Alfarería Baker with cover/handles Molde de Hornear conTapa/Asas Polish Pottery Alfarería Polaca

This example shows that when appropriately “chunked,” machinetranslation grammar knowledge for noun phrases can be minimized.However, it cannot be eliminated entirely.

Referring to FIG. 3, the Normalized Special Terms and Phrases repository316 contains chunked content that is in a form that supports manualtranslation. It is free of unusual acronyms, misspellings, and strivedfor consistency. In Table 3 for example, Emile Henry was also listed asE. Henry. Terms usage is maximized.

The Special Terms and Phrases Translation Dictionary repository 318 isthe translated normalized terms and phrases content. It is the specialtydictionary for the client content.

Other translation dictionaries 320 may be any of various commerciallyavailable dictionary tools and/or SOLx developed databases. They may begeneral terms dictionaries, industry specific, SOLx acquired content, orany other knowledge that helps automate the process.

One of the tenets of the SOLx process is that the original content neednot be altered. Certainly, there are advantages to make the content asinternally consistent as possible, and to define some form of structureor syntax to make translations easier and more accurate. However, thereare situations where a firm's IT department does not want the originalcontent modified in any way. Taking advantage of the benefits ofnormalized content, but without actually modifying the original, SOLxuses a set of meta or non-persistent stores so that the translations arebased on the normalized meta content 322.

The above discussion suggests a number of processes that may beimplemented for the automatic translation of large databases ofstructured content. One implementation of these processes is illustratedin the flow chart of FIG. 4 and is summarized below. It will beappreciated that these processes and the ordering thereof can bemodified.

First, the firm's IT organization extracts 400 the content from their ITsystems—ideally with a part number or other unique key. As discussedabove, one of the key SOLx features is that the client need notrestructure or alter the original content in their IT databases.However, there are reasons to do so. In particular, restructuringbenefits localization efforts by reducing the translation set up timeand improving the translation accuracy. One of these modifications is toadopt a ‘normalized’ or fixed syntactic, semantic, and grammaticaldescription of each content entry.

Next, software tools identify (402) the most important terms andphrases. Nearest neighbor, filtered N-gram, and other analysis toolsidentify the most used and important phrases and terms in the content.The content is analyzed one description or item at a time and re-usablechunks are extracted.

Subject matter experts then “internationalize” (404) the important termsand phrases. These experts “translate” the abbreviations and acronyms,correct misspellings and in general redefine and terms that would beambiguous for translation. This is a list of normalized terms andphrases. It references the original list of important terms and phrases.

Translators can then translate (406) the internationalized importantterms and phrases. This translated content forms a dictionary ofspecialty terms and phrases. In essence, this translated contentcorresponds to the important and re-usable chunks. Depending on thetranslation engine used, the translator may need to specify the genderalternatives, plural forms, and other language specific information forthe special terms and phrases dictionary. Referring again to an examplediscussed above, translators would probably supply the translation for(four-strand), (color-coded), (twisted-pair), telephone, and wire. Thisassumes that each term was used repeatedly. Any other entry that uses(color-coded) or wire would use the pre-translated term.

Other dictionaries for general words and even industry specificnomenclature can then be consulted (408) available. This same approachcould be used for the creation of general dictionaries. However, forpurposes of this discussion it is assumed that they already exist.

Next, language specific rules are used to define (410) the assembly oftranslated content pieces. The types of rules described above define theway the pre-translated chunks are reassembled. If, in any onedescription, the grammatical structure is believed to be morecomplicated than the pre-defined rule set, then the phrase is translatedin its entirety.

The original content (on a per item basis) is then mapped (412) againstthe dictionaries. Here, the line item content is parsed and thedictionaries are searched for the appropriate chunked and more generalterms (content chunks to translated chunks). Ideally, all terms in thedictionaries map to a single-line item in the content database, i.e. asingle product description. This is the first function of thetranslation engine.

A software translation engine then assembles (414) the translated piecesagainst the language rules. Input into the translation engine includesthe original content, the translation or assembly rules, and thetranslated pieces. A translation tool will enable a translator tomonitor the process and directly intercede if required. This couldinclude adding a new chunk to the specialty terms database, oroverriding the standard terms dictionaries.

A statistically based software tool assesses (416) the potentialaccuracy of the translated item. One of the difficulties of translationis that when something is translated from one language to another andthen retranslated back to the first, the original content is rarelyreproduced. Ideally, one hopes it is close, but rarely will it be exact.The reason for this is there is not a direct inverse in languagetranslation. Each language pair has a circle of ‘confusion’ oracceptability. In other words, there is a propagation of error in thetranslation process. Short of looking at every translated phrase, thebest than can be hoped for in an overall sense is a statisticalevaluation.

Translators may re-edit (418) the translated content as required. Sincethe content is stored in a database that is indexed to the originalcontent on an entry-by-entry basis, any entry may be edited and restoredif this process leads to an unsatisfactory translation.

Although not explicitly described, there are terms such as proper nouns,trade names, special terms, etc., that are never translated. Theidentification of these invariant terms would be identified in the aboveprocess. Similarly, converted entries such as metrics would be handledthrough a metrics conversion process.

The process thus discussed uses both human and machine translation in adifferent way than traditionally employed. This process, with thecorrect software systems in place should generate much of the accuracyassociated with manual translation. Further, this process shouldfunction without manual intervention once sufficient content has beenpre-translated.

The various configuration processes are further illustrated by thescreenshots of FIGS. 5-10. Although these figures depict screenshots, itwill be appreciated that these figures would not be part of the userinterface as seen by an SME or other operator. Rather, these screenshotsare presented here for purposes of illustration and the associatedfunctionality would, to a significant extent, be implementedtransparently. These screenshots show the general processing of sourcecontent. The steps are importing the data, normalizing the data based ona set of grammars and rules produced by the SME using the NTW userinterface, then analysis of the content to find phrases that need to betranslated, building a translation dictionary containing the discoveredphrases, translation of the normalized content, and finally, estimationof the quality of the translated content.

The first step, as illustrated in FIG. 5 is to import the sourcestructured content file. This will be a flat set file with the propercharacter encoding, e.g., UTF-8. There will generally be one itemdescription per line. Some basic formatting of the input may be done atthis point.

FIG. 6 shows normalized form of the content on the right and theoriginal content (as imported above) on the left. What is not shown hereare the grammars and rules used to perform the normalization. The formof the grammars and rules and how to created them are described above.

In this example, various forms of the word resistor that appear on theoriginal content, for example “RES” or RESS” have been normalized to theform “resistor”. The same is true for “W” being transformed to “watt”and “MW” to “milliwatt”. Separation was added between text items, forexample, “¼W” is now “¼ watt” or “750HM” is now “75 ohm”. Punctuationcan also be added or removed, for example, “RES,35.7” is now “resistor35.7”. Not shown in the screenshot: the order of the text can also bestandardized by the normalization rules. For example, if the user alwayswant a resistor description to of the form:

-   -   resistor<ohms rating><tolerance><watts rating>        the normalization rules can enforce this standard form, and the        normalized content would reflect this structure.

Another very valuable result of the normalization step can be to createa schematic representation of the content. In the phrase analysis step,as illustrated, the user is looking for the phrases in the nownormalized content that still need to be translated to the targetlanguage. The purpose of Phrase Analysis, and in fact, the next severalsteps, is to create a translation dictionary that will be used bymachine translation. The value in creating the translation dictionary isthat only the phrases need translation not the complete body of text,thus providing a huge savings in time and cost to translate. The PhraseAnalyzer only shows us here the phrases that it does not already have atranslation for. Some of these phrases we do not want to translate,which leads us to the next step.

In the filter phrases step as shown in FIG. 7, an SME reviews thisphrase data and determines which phrases should be translated. Once theSME has determined which phrases to translate, then a professionaltranslator and/or machine tool translates the phrases (FIGS. 8-9) fromthe source language, here English, to the target language, here Spanish.A SOLx user interface could be used to translate the phrases, or thephrases are sent out to a professional translator as a text file fortranslation. The translated text is returned as a text file and loadedinto SOLx. The translated phrases become the translation dictionary thatis then used by the machine translation system.

The machine translation system uses the translation dictionary createdabove as the source for domain specific vocabulary. By providing thedomain specific vocabulary in the form of the translation dictionary,The SOLx system greatly increases the quality of the output from themachine translation system.

The SOLx system can also then provide an estimation of the quality ofthe translation result (FIG. 10). Good translations would then be loadedinto the run-time localization system for use in the source systemarchitecture. Bad translations would be used to improve thenormalization grammars and rules, or the translation dictionary. Thegrammars, rules, and translation dictionary form a model of the content.Once the model of the content is complete, a very high level oftranslations are of good quality.

Particular implementations of the above described configurationprocesses can be summarized by reference to the flow charts of FIGS.11-12. Specifically, FIG. 11 summarizes the steps of an exemplarynormalization configuration process and FIG. 12 summarizes an exemplarytranslation configuration process.

Referring first to FIG. 11, a new SOLx normalization process (1000) isinitiated by importing (1102) the content of a source database orportion thereof to be normalized and selecting a quantify of text from asource database. For example, a sample of 100 item descriptions may beselected from the source content “denoted content.txt file.” A texteditor may be used to select the 100 lines. These 100 lines are thensaved to a file named samplecontent.txt for purposes of this discussion.

The core items in the samplecontent.txt file are then found (1104) usingthe Candidate Search Engine, for example, by running a words-in-commonsearch. Next, attribute/value information is found (1106) in thesamplecontent.txt file using the Candidate Search Engine by runningcollocation and semantic category searches as described above. Once theattributes/values have been identified, the SOLx system can be used towrite (1108) attribute rules. The formalism for writing such rules hasbeen discussed above. It is noted that the SOLx system performs much ofthis work for the user and simple user interfaces can be provided toenable “writing” of these rules without specialized linguistic ordetailed code-writing skills. The SOLx system can also be used at thispoint to write (1110) categorization rules. As noted above, suchcategorization rules are useful in defining a context for avoiding orresolving ambiguities in the transformation process. Finally, thecoverage of the data set can be analyzed (1112) to ensure satisfactoryrun time performance. It will be appreciated that the configurationprocess yields a tool that can not only translate those “chunks” thatwere processed during configuration, but can also successfully translatenew items based on the knowledge base acquired and developed duringconfiguration. The translation process is summarized below.

Referring to FIG. 12, the translation process 1200 is initiated byacquiring (1202) the total set of item descriptions that you want totranslate as a flat file, with a single item description per line. Forpurposes of the present discussion, it is assumed that the itemdescriptions are in a file with the name of content.txt. A text editormay be used to setup an associated project configuration file.

Next, a sample of 100 item descriptions is selected (1204) from thecontent.txt file. A text editor may be used to select the 100 lines.These 100 lines to a file named samplecontent.txt.

The translation process continues with finding (1206) candidates forvocabulary adjustment rules in the samplecontent.txt file using theCandidate Search Engine. The Candidate Search Engine may implement acase variant search and full/abbreviated variant search at this point inthe process. The resulting information can be used to write vocabularyadjustment rules. Vocabulary adjustment rules may be written to convertabbreviated forms to their full forms.

Next, candidates for labeling/tagging rules are found (1208) in thesample/content.txt file using the Candidate Search Engine.Labeling/tagging rules may be written to convert semantic category andcollocation forms. Attribute rules can then be written (1210) followingthe steps set forth in the previous flow chart.

Vocabulary adjustment rules are then run (1212) using the NaturalLanguageEngine against the original content. Finally, the coverage ofthe data set can be analyzed (1214) evaluating performance of yourvocabulary adjustment rules and evaluating performance of your attributerules. At this point, if the proper coverage is being achieved by thevocabulary adjustment rules, then the process proceeds to building(1216) a domain-specific dictionary. Otherwise, a new set of 100 itemdescriptions can be selected for analysis and the intervening steps arerepeated.

To build a domain specific dictionary, the SME can run a translationdictionary creation utility. This runs using the rule files createdabove as input, and produces the initial translation dictionary file.This translation dictionary file contains the words and phrases thatwere found in the rules. The words and phrases found in the translationdictionary file can then be manually and/or machine translated (1218).This involves extracting a list of all word types using a text editorand then translating the normalized forms manually or through a machinetool such as SYSTRAN. The translated forms can then be inserted into thedictionary file that was previously output.

Next, the SME can run (1220) the machine translation module, run therepair module, and run the TOE module. The file outputs from TOE arereviewed (1222) to determine whether the translation results areacceptable. The acceptable translated content can be loaded (1224) intothe Localized Content Server (LCS), if desired. The remainder of thetranslated content can be analyzed (1226) to determine what changes tomake to the normalization and translation knowledge bases in order toimprove the quality of the translation. Words and phrases that should bedeleted during the translation process can be deleted (1228) andpart-of-speech labels can be added, if needed. The SME can then create(1230) a file containing the translated words in the source and targetlanguages. Once all of the content is found to be acceptable, thesystems is fully trained. The good translated content is then loadedinto the LCS.

It has been found that it is useful to provide graphical feedback duringnormalization to assist the SME in monitoring progress. Any appropriateuser interface may be provided in this regard. FIG. 13 shows an exampleof such an interface. As shown, the graphical desktop 1300 is dividedinto multiple work spaces, in this case, including workspaces 1302, 1304and 1306. One workspace 1302 presents the source file content that is inprocess, e.g., being normalized and translated. A second area 1304, inthis example, functions as the normalization workbench interface and isused to perform the various configuration processes such as replacingvarious abbreviations and expressions with standardized terms or, in theillustrated example, defining a parse tree. Additional workspaces suchas workspace 1306 may be provided for accessing other tools such as theCandidate Search Engine which can identify terms for normalization or,as shown, allow for selection of rules. In the illustrated example,normalized terms are highlighted relative to the displayed source filein workspace 1302 on a currently updated basis. In this manner, the SMEcan readily determine when all or enough of the source file has beennormalized.

In a traditional e-business environment, this translation processessentially is offline. It becomes real-time and online when new contentis added to the system. In this case, assuming well-developedspecial-purpose dictionaries and linguistic information already exists,the process can proceed in an automatic fashion. Content, oncetranslated is stored in a specially indexed look-up database. Thisdatabase functions as a memory translation repository. With this type ofstorage environment, the translated content can be scaled to virtuallyany size and be directly accessed in the e-business process. Theassociated architecture for supporting both configuration and run-timeoperation is discussed below.

B. SOLx Architecture

1. Network Architecture Options

The SOLx system operates in two distinct modes. The “off-line” mode isused to capture knowledge from the SME/translator and knowledge aboutthe intended transformation of the content. This collectively defines aknowledge base. The off-line mode includes implementation of theconfiguration and translation processes described above. Once theknowledge base has been constructed, the SOLx system can be used in afile in/file out manner to transform content.

The SOLx system may be implemented in a variety of business-to-business(B2B) or other frameworks, including those shown in FIG. 14. Here theSource 1402, the firm that controls the original content 1404, can beinterfaced with three types of content processors 1406. The SOLx system1400 can interface at three levels: with a Local Platform 1408(associated with the source 1402), with a Target Platform 1410(associated with a target to whom the communication is addressed or isotherwise consumed by) and with a Global Platform 1412 (separate fromthe source 1402 and target 1408).

A primary B2B model of the present invention focuses on a Source/Sellermanaging all transformation/localization. The Seller will communicatewith other Integration Servers (such as WebMethods) and bareapplications in a “Point to Point” fashion, therefore, all locales anddata are registered and all localization is done on the seller side.However, all or some of the localization may be managed by the buyer oron a third party platform such as the global platform.

Another model, which may be implemented using the global server, wouldallow two SOLx B2B-enabled servers to communicate in a neutralenvironment, e.g. English. Therefore, a Spanish and a Japanese systemcan communicate in English by configuring and registering the localcommunication in SOLx B2B.

A third model would include a local seller communicating directly (viaHTTP) with the SOLx B2B enabled Buyer.

2. Network Interface

Previously, it was discussed how structured content is localized. Thenext requirement is to rapidly access this content. If there are ongoingrequests to access a particular piece of localized content, it may beinefficient to continually translate the original entry. The issues, ofcourse, are speed and potentially quality assurance. One solution is tostore the translated content along with links to the original with avery fast retrieval mechanism for accessing the translated content. Thisis implemented by the SOLx Globalization Server.

The SOLx Globalization server consists of two major components (1) theDocument Processing Engine and (2) the Translated Content Server (TCS).The Document Processing Engine is a WebMethods plug-compatibleapplication that manages and dispenses localized content throughXML-tagged business objects. The TCS contains language-paired contentthat is accessed through a cached database. This architecture assuresvery high-speed access to translated content.

This server uses a hash index on the translated content cross-indexedwith the original part number or a hash index on the equivalent originalcontent, if there is not a unique part number. A direct link between theoriginal and translated content via the part number (or hash entry)assures retrieval of the correct entry. The indexing scheme alsoguarantees very fast retrieval times. The process of adding a newlocalized item to the repository consists of creating the hash index,link to the original item, and its inclusion into the repository. TheTCS will store data in Unicode format.

The TCS can be used in a standalone mode where content can be accessedby the SKU or part number of the original item, or through text searchesof either the original content or its translated variant. If the hashedindex of the translated content is known. It, of course, can be assessedthat way. Additionally, the TCS will support SQL style queries throughthe standard Oracle SQL query tools.

The Document Processing Engine is the software component of theGlobalization Server that allows localized content in the TCS to beintegrated into typical B2B Web environments and system-to-systemtransactions. XML is rapidly replacing EDI as the standard protocol forWeb-based B2B system-to-system communication. There are a number of coretechnologies often call “adaptors” or “integration servers” thattranslate ERP content, structures, and formats, from one systemenvironment to another. WebMethods is one such adaptor but any suchtechnology may be employed.

FIG. 15 shows a conventional web system 1500 where, the WebMethodsintegration server 1502 takes as input an SAP-formatted content calledan IDOC 1504 from a source back office 1501 via API 1503 and converts itinto an XML-formatted document 1506 for transmission over the Web 1508via optional application server 1510 and HTTP servers 1512 to some otherreceiver such as a Target back office 1510 or other ERP system. Thedocument 1506 may be transmitted to Target back office 1514 via HTTPservers 1516 and an integration server 1518.

FIG. 16 shows the modification of such a system that allows the TCS 1600containing translated content to be accessed in a Web environment. Inthis figure, original content from the source system 1602 is translatedby the NorTran Server 1604 and passed to a TCS repository 1606. Atransaction request, whether requested from a foreign system or thesource system 1602, will pass into the TCS 1600 through the DocumentProcessing Engine 1608. From there, a communication can be transmittedacross the Web 1610 via integration server adaptors 1612, an integrationserver 1614, an optional application server 1616 and HTTP servers 1618.

3. SOLx Component Structure

FIG. 17 depicts the major components of one implementation of the SOLxsystem 1700 and the SOLx normalization/translation processes asdiscussed above. The NorTran Workbench/Server 1702 is that component ofthe SOLx system 1700 that, under the control of a SME/translator 1704,creates normalized/translated content. The SOLx Server 1708 isresponsible for the delivery of content either as previously cachedcontent or as content that is created from the real-time application ofthe knowledge bases under control of various SOLx engines.

The initial step in either a normalization or translation process is toaccess legacy content 1710 that is associated with the firms' variouslegacy systems 1712. The legacy content 1710 may be provided as level 1commerce data consisting of short descriptive phrases delivered as flatfile structures that are used as input into the NorTran Workbench 1702.

There are a number of external product and part classification schemas1714, both proprietary and public. These schemas 1714 relate one classof part in terms of a larger or more general family, a taxonomy of partsfor example. These schemas 1714 define the attributes that differentiateone part class from another. For example, in bolts, head style is anattribute for various types of heads such as hex, fillister, Phillips,etc. Using this knowledge in the development of the grammar rules willdrastically shorten the time to normalize large quantities of data.Further, it provides a reference to identify many of the synonyms andabbreviations that are used to describe the content.

The NorTran Workbench (NTW) 1702 is used to learn the structure andvocabulary of the content. The NTW user interface 1716 allows the SME1704 to quickly provide the system 1700 with knowledge about thecontent. This knowledge is captured in the form of content parsinggrammars, normalization rules, and the translation dictionary. As theSME 1704 “trains” the system 1700 in this manner, he can test to see howmuch of the content is understood based on the knowledge acquired sofar. Once the structure and vocabulary are well understood, in otherwords an acceptable coverage has been gained, then NTW 1702 is used tonormalize and translate large quantities of content.

Thus, one purpose of NTW 1702 is to allow SMEs 1704 to use a visual toolto specify rules for parsing domain data and rules for writing outparsed data in a normalized form. The NTW 1702 allows the SME 1704 tochoose data samples from the main domain data, then to select a line ata time from that sample. Using visual tools such as drag and drop, andconnecting items on a screen to establish relationships, the SME 1704can build up parse rules that tell the Natural Language Engine (NLE)1718 how to parse the domain data. The SME 1704 can then use visualtools to create rules to specify how the parsed data will be assembledfor output—whether the data should be reordered, how particular groupsof words should be represented, and so on. The NTW 1702 is tightlyintegrated with the NLE 1718. While the NTW 1702 allows the user toeasily create, see, and edit parse rules and normalization rules, theNLE 1718 creates and stores grammars from these rules.

Although content parsing grammars, normalization rules, and contexttokens constitute the core knowledge created by the SME 1704 using thesystem 1700, the GUI 1716 does not require the SME 1704 to have anybackground in computational linguistic, natural language processing orother abstract language skill whatsoever. The content SME 1704 mustunderstand what the content really is, and translators must be technicaltranslators. A “butterfly valve” in French does not translate to theFrench words for butterfly and valve.

The CSE 1720 is a system initially not under GUI 1716 control thatidentifies terms and small text strings that repeat often throughout thedata set and are good candidates for the initial normalization process.

One purpose of this component is to address issues of scale in findingcandidates for grammar and normalization rules. The SOLx system 1700provides components and processes that allow the SME 1704 to incorporatethe knowledge that he already has into the process of writing rules.However, some domains and data sets are so large and complex that theyrequire normalization of things other than those that the SME 1704 isalready aware of. Manually discovering these things in a large data setis time-consuming and tedious. The CSE 1720 allows automatic applicationof the “rules of thumb” and other heuristic techniques that dataanalysts apply in finding candidates for rule writing.

The CSE component works through the programmatic application ofheuristic techniques for the identification of rule candidates. Theseheuristics were developed from applying knowledge elicitation techniquesto two experienced grammar writers. The component is given a body ofinput data, applies heuristics to that data, and returns a set of rulecandidates.

The N-Gram Analysis (NGA) lexical based tool 1722 identifies word andstring patterns that reoccur in the content. It identifies single andtwo and higher word phrases that repeat throughout the data set. It isone of the core technologies in the CSE 1720. It is also used toidentify those key phrases that should be translated after the contenthas been normalized.

The N-Gram Analysis tool 1722 consists of a basic statistical engine,and a dictionary, upon which a series of application engines rely. Theapplications are a chunker, a tagger, and a device that recognizes thestructure in structured text. FIG. 18 shows the relationships betweenthese layers.

One purpose of the base N-Gram Analyzer component 1800 is to contributeto the discovery of the structure in structured text. That structureappears on multiple levels, and each layer of the architecture works ona different level. The levels from the bottom up are “words”, “terms”,“usage”, and “dimensions of schema”. The following example shows thestructure of a typical product description.

acetone amber glass bottle, assay >99.5% color (alpha) <11

The word-level of structure is a list of the tokens in the order oftheir appearance. The word “acetone” is first, then the word “amber”,and so forth.

The terminology-level of structure is a list of the groups of words thatact like a single word. Another way of describing terminology is to saythat a group of words is a term when it names a standard concept for thepeople who work in the subject matter. In the example, “acetone”, “amberglass”, and “color (alpha)” are probably terms.

The next two levels of structure connect the words and terms to the goalof understanding the product description. The SOLx system approximatesthat goal with a schema for understanding. When the SOLx system operateson product description texts, the schema has a simple form that repeatsacross many kinds of products. The schema for product descriptions lookslike a table.

Quantity/ Product Where Used Color Package . . . pliers non sterileblack 1 . . . forceps sterile silver 6 . . . paint exterior red 1 . . .. . . . . . . . . . . . . . .Each column of the table is a property that characterizes a product.Each row of the table is a different product. In the cells of the roware the particular values of each property for that product. Differentcolumns may be possible for different kinds of products. This reportrefers to the columns as “dimensions” of the schema. For other subjectmatter, the schema may have other forms. This fragment does not considerthose other forms.

Returning to the example, the next level of structure is the usagelevel. That level classifies each word or term according to thedimension of the schema that it can describe. In the example, “acetone”is a “chemical”; “amber glass” is a material; “bottle” is a “product”;and so forth. The following tagged text shows the usage level ofstructure of the example in detail.

[chemical](acetone) [material](amber glass) [product](bottle) [,](,)

[measurement](assay) [>](>) [number](99) [.](.) [number](5)

[unit_of_measure](%) [measurement](color (alpha)) [<](<) [number](11)

The top level of structure that SOLx considers for translation consistsof the dimensions of the schema. At that level, grammatical sequences ofwords describe features of the product in some dimensions that arerelevant to that product. In the example, “acetone” describes thedimension “product”; “amber glass bottle” describes a “container ofproduct”; and so forth. The following doubly tagged text shows thedimension-level of structure for the example, without identifying thedimensions.

[schema]([chemical](acetone))

[schema]([material](amber glass) [product](bottle) [,](,))

[schema]([measurement](assay) [>](>) [number](99) [.](.[) [number](5)

[unit_of_measure](%))

[schema]([measurement](color (alpha)) [<](<)[number](11))

Given the structure above, it is possible to insert commas into theoriginal text of the example, making it more readable. The followingtext shows the example with commas inserted.

acetone, amber glass bottle, assay >99.5%, color (alpha) <11

This model of the structure of text makes it possible to translate moreaccurately.

The discovery of structure by N-Gram Analysis is parallel to thediscovery of structure by parsing in the Natural Language Engine. Thetwo components are complementary, because each can serve where the otheris weak. For example, in the example above, the NLE parser coulddiscover the structure of the decimal number, “[number](99.5)”, savingNGA the task of modeling the grammar of decimal fractions. Thestatistical model of grammar in NGA can make it unnecessary for humanexperts to write extensive grammars for NLE to extract a diverselarger-scale grammar. By balancing the expenditure of effort in NGA andNLE, people can minimize the work necessary to analyze the structure oftexts.

One of the basic parts of the NGA component 1800 is a statisticalmodeler, which provides the name for the whole component. Thestatistical idea is to count the sequences of words in a body of text inorder to measure the odds that a particular word appears after aparticular sequence. In mathematical terms, the statistical modelercomputes the conditional probability of word n, given words 1 throughn−1: P(w_(n)|w₁, . . . , w_(n−n)).

Using that statistical information about a body of text, it is possibleto make reasonable guesses about the structure of text. The firstapproximation of a reasonable guess is to assume that the most likelystructure is also the structure that the author of the text intended.That assumption is easily incorrect, given the variety of human authors,but it is a good starting place for further improvement.

The next improvement toward recognizing the intent of the author is toadd some specific information about the subject matter. The dictionarycomponent 1802 captures that kind of information at the levels of words,terms, and usage. Two sources may provide that information. First, ahuman expert could add words and terms to the dictionary, indicatingtheir usage. Second, the NLE component could tag the text, using itsgrammar rules, and the NGA component adds the phrases inside the tags tothe dictionary, using the name of the tag to indicate the usage.

The information in the dictionary complements the information in thestatistical model by providing a better interpretation of text when thestatistical assumption is inappropriate. The statistical model acts as afallback analysis when the dictionary does not contain information aboutparticular words and phrases.

The chunker 1804 combines the information in the dictionary 1802 and theinformation in the statistical model to partition a body of texts intophrases. Partitioning is an approximation of parsing that sacrificessome of the details of parsing in order to execute without the grammarrules that parsing requires. The chunker 1804 attempts to optimize thepartitions so each cell is likely to contain a useful phrase. One partof that optimization uses the dictionary to identify function words andexcludes phrases that would cut off grammatical structures that involvethe function words.

The chunker can detect new terms for the dictionary in the form of cellsof partitions that contain phrases that are not already in thedictionary. The output of the chunker is a list of cells that it used topartition the body of text.

The tagger 1806 is an enhanced form of the chunker that reports thepartitions instead of the cells in the partitions. When a phrase in acell of a partition appears in the dictionary, and the dictionary entryhas the usage of the phrase, the tagger prints the phrase with the usagefor a tag. Otherwise, the tagger prints the phrase without a tag. Theresult is text tagged with the usage of the phrases.

The structurer 1808 uses the statistical modeler to determine how todivide the text into dimensions of the schema, without requiring aperson to write grammar rules. The training data for the structurer'sstatistical model is a set of tagged texts with explicit “walls” betweenthe dimensions of the schema. The structurer trains by using the N-GramAnalyzer 1800 to compute the conditional probabilities of the walls inthe training data. The structurer 1808 operates by first tagging a bodyof text and then placing walls into the tagged text where they are mostprobable.

Referring again to FIG. 17, the candidate heuristics are a series ofknowledge bases, much like pre-defined templates that kick-start thenormalization process. They are intended to address pieces of contentthat pervade user content. Items such as units of measure, powerconsumption, colors, capacities, etc. will be developed and semanticcategories 1724 are developed.

The spell checker 1726 is a conventional module added to SOLx toincrease the effectiveness of the normalization.

The Grammar & Rules Editor (GRE) 1728 is a text-editing environment thatuses many Unix like tools for creation of rules and grammars fordescribing the content. It can always be used in a “all-back” situation,but will rarely be necessary when the GUI 1716 is available.

The Taxonomy, Schemas, & Grammar Rules module 1730 is the output fromeither the GRE 1728 or the GUI 1716. It consists of a set of ASCII filesthat are the input into the natural language parsing engine (NLE) 1718.

On initialization, the NLE 1718 reads a set of grammar and normalizationrules from the file system or some other persistent storage medium andcompiles them into a set of Rule objects employed by the runtimetokenizer and parser and a set of NormRule objects employed by thenormalizer. Once initialized the NLE 1718 will parse and normalize inputtext one line at a time or may instead process a text input file inbatch mode, generating a text output file in the desired form. Thestorage medium includes a memory.

Configuration and initialization generally requires that a configurationfile be specified. The configuration file enumerates the contents of theNLE knowledge base, providing a list of all files containing format,grammar, and normalization rules.

NLE 1718 works in three steps: tokenization, parsing, and normalization.First, the input text is tokenized into one or more candidate tokensequences. Tokenization is based on what sequences of tokens may occurin any top-level phrase parsed by the grammar. Tokens must be delineatedby white space unless one or more of such tokens are represented asregular expressions in the grammar, in which case the tokens may becontiguous, undelineated by white space. Tokenization may yieldambiguous results, i.e., identical strings that may be parsed by morethan one grammar rule. The parser resolves such ambiguities.

The parser is a modified top-down chart parser. Standard chart parsersassume that the input text is already tokenized, scanning the string oftokens and classify each according to its part-of-speech or semanticcategory. This parser omits the scanning operation, replacing it withthe prior tokenization step. Like other chart parsers, it recursivelypredicts those constituents and child constituents that may occur perthe grammar rules and tries to match such constituents against tokensthat have been extracted from the input string. Unlike the prototypicalchart parser, it is unconstrained where phrases may begin and end, howoften they may occur in an input string, or some of the input text mightbe unable to be parsed. It generates all possible parses that occur,starting at any arbitrary white space delineated point in the inputtext, and compares all possible parse sequences, selecting the bestscoring alternative and generating a parse tree for each. If more thanone parse sequence achieves the best score, both parse trees areextracted from the chart and retained. Others are ignored.

Output of the chart parser and the scoring algorithm is the set ofalternative high scoring parse trees. Each parse tree object includesmethods for transforming itself according to a knowledge base ofnormalization rules. Each parse tree object may also emit a Stringcorresponding to text contained by the parse tree or such a Stringtogether with a string tag. Most such transformation or emission methodstraverse the parse tree in post-order, being applied to a parse tree'schildren first, then being applied to the tree itself. For example, atostringo method collects the results of tostring( ) for each child andonly then concatenates them, returning the parse tree's Stringrepresentation. Thus, normalization and output is accomplished as a setof traversal methods inherent in each parse tree. Normalization includesparse tree transformation and traversal methods for replacing orreordering children (rewrite rules), for unconditional or lookup tablebased text replacement, for decimal punctuation changes, for joiningconstituents together with specified delimiters or without white space,and for changing tag labels.

The Trial Parsed Content 1734 is a set of test samples of either taggedor untagged normalized content. This sample corresponds to a set ofrules and grammars that have been parsed. Trial parsed content is theoutput of a statistical sample of the original input data. When asequence of content samples parses to a constant level of unparsedinput, then the set if grammars and rules are likely to be sufficientlycomplete that the entire data may be successfully parsed with a minimumof ambiguities and unparsed components. It is part of the interactiveprocess to build grammars and rules for the normalization of content.

A complete tested grammar and rule set 1736 corresponding to the fullunambiguous tagging of content is the goal of the normalization process.It insures that all ambiguous terms or phrases such as Mil that could beeither a trade name abbreviation for Milwaukee or an abbreviation forMilitary have been defined in a larger context. This set 1736 is thengiven as input to the NLE Parsing Engine 1738 that computes the finalnormalized content, and is listed in the figure as Taxonomy TaggedNormalized Content 1732.

The custom translation dictionary 1740 is a collection of words andphrases that are first identified through the grammar rule creationprocess and passed to an external technical translator. This content isreturned and is entered into one of the custom dictionaries associatedwith the machine translation process. There are standard formats thattranslators typically use for sending translated content.

The MTS 1742 may be any of various conventional machine translationproducts that given a set of custom dictionaries as well as its standardones, a string of text in one language, produces a string of test in thedesired language. Current languages supported by one such product markedunder the name SYSTRAN include: French, Portuguese, English, German,Greek, Spanish, Italian, simplified Chinese, Japanese, and Korean.Output from the MTS is a Translated Content file 1744.

The one purpose of the Machine Translation Server 1742 is to translatestructured texts, such as product descriptions. The state of the art incommercial machine translation is too weak for many practicalapplications. The MTS component 1742 increases the number ofapplications of machine translation by wrapping a standard machinetranslation product in a process that simplifies its task. Thesimplification that MTS provides comes from its ability to recognize thestructure of texts to be translated. The MTS decomposes the text to betranslated into its structural constituents, and then applies machinetranslation to the constituents, where the translation problem issimpler. This approach sacrifices the fidelity of references betweenconstituents in order to translate the individual constituentscorrectly. For example, adjective inflections could disagree with thegender of their objects, if they occur in different constituents. Thecompromise results in adequate quality for many new applications inelectronic commerce. Future releases of the software will address thisissue, because the compromise is driven by expedience.

The conditioning component of MTS 1742 uses the NGA component torecognize the structure of each text to be translated. It prepares thetexts for translation in a way that exploits the ability of the machinetranslation system to operate on batches of texts. For example, SYSTRANcan interpret lists of texts delimited by new-lines, given a parameterstating that the document it receives is a parts list. Within each lineof text, SYSTRAN can often translate independently between commas, sothe conditioning component inserts commas between dimensions of theschema if they are not already present. The conditioning component maycompletely withhold a dimension from machine translation, if it has acomplete translation of that dimension in its dictionary.

The machine translation component provides a consistent interface for avariety of machine translation software products, in order to allowcoverage of language pairs.

The repair component is a simple automated text editor that removesunnecessary words, such as articles, from SYSTRAN's Spanish translationsof product descriptions. In general, this component will correct forsmall-scale stylistic variations among machine translation tools.

The Translation Quality Estimation Analyzer (TQA) 1746 merges thestructural information from conditioning with the translations fromrepair, producing a list of translation pairs. If any phrases bypassedmachine translation, this merging process gets their translations fromthe dictionary.

After merging, translation quality estimation places each translationpair into one of three categories. The “good” category contains pairswhose source and target texts have acceptable grammar, and the contentof the source and target texts agrees. A pair in the “bad” category hasa source text with recognizable grammar, but its target grammar isunacceptable or the content of the source text disagrees with thecontent of the target text. The “ugly” category contains pairs whosesource grammar is unfamiliar.

The feedback loop extracts linguistic knowledge from a person. Theperson examines the “bad” and “ugly” pairs and takes one of thefollowing actions. The person may define words and terms in thedictionary, indicating their usage. The person may define grammar rulesfor the NLE component in order to tag some part of the text. The personmay correct the translation pair (if it requires correction), and placeit into the set of examples for training the translation qualityestimation models. The person may take the source text, mark it withwalls between dimensions of the schema, and place it into the set ofexamples for training the structure model. An appropriate graphical userinterface will make the first and last actions implicit in the thirdaction, so a person will only have to decide whether to write grammarsor to correct examples.

The translation quality estimation component uses two models from theN-Gram Analyzer that represent the grammar of the source and targettexts. The translation quality estimation component also uses a contentmodel that is partially statistical and partially the dictionary. Thetwo parts overlap in their ability to represent the correspondence incontent between source and target texts. The dictionary can representexact correspondences between words and terms. The statistical model canrecognize words that occur in one language, but are unnecessary in theother, and other inexact correspondences.

It is well known that the accuracy of machine translations based onstandard glossaries are only sufficient to get the gist of thetranslation. There are no metrics associated with the level of accuracyof any particular translation. The TQA 1746 attempts to define a measureof accuracy for any single translation. The basis for the accuracyestimate is a statistical overlap between the translated content at theindividual phrase level, and prior translations that have been manuallyevaluated.

The Normalized Content 1748 and/or Translated Content 1706 can next becached in the Normalized Content Server and Localized Content Server(LCS) 1752, respectively. This cached data is made available through theSOLx Server 1708.

The LCS 1752 is a fast lookup translation cache. There are two parts tothe LCS 1752: an API that is called by Java clients (such as a JSPserver process) to retrieve translations, and an user interface 1754that allows the user 1756 to manage and maintain translations in the LCSdatabase 1752.

As well as being the translation memory foundation of the SOLx system1700, the LCS 1752 is also intended to be used as a standalone productthat can be integrated into legacy customer servers to providetranslation lookups.

The LCS 1752 takes as input source language text, the source locale, andthe target locale. The output from LCS 1752 is the target text, ifavailable in the cache, which represents the translation from the sourcetext and source locale, into the target locale. The LCS 1752 is loadedahead of run-time with translations produced by the SOLx system 1700.The cache is stored in a relational database.

The SOLx Server 1708 provides the customer with a mechanism for run-timeaccess to the previously cached, normalized and translated data. TheSOLx Server 1708 also uses a pipeline processing mechanism that not onlypermits access to the cached data, but also allows true on-the-flyprocessing of previously unprocessed content. When the SOLx Serverencounters content that has not been cached, it then performs thenormalization and/or translation on the fly. The existing knowledge baseof the content structure and vocabulary is used to do the on-the-flyprocessing.

Additionally, the NCS and LCS user interface 1754 provides a way forSMEs 1756 to search and use normalized 1748 and translated 1706 data.The NCS and LCS data is tied back to the original ERP information viathe customer's external key information, typically an item part number.

As shown in FIG. 1700, the primary NorTran Workbench engines are alsoused in the SOLx Server 1708. These include: N-Gram Analyzer 1722,Machine Translation Server 1742, Natural Language Engine 1718, CandidateSearch Engine 1720, and Translation Quality Analyzer 1746. The SOLxserver 1708 also uses the grammar rules 1754 and custom and standardglossaries 1756 from the Workbench 1702. Integration of the SOLx server1708 for managing communication between the source/legacy system 1712and targets via the Web 1758 is managed by an integration server 1758and a workflow control system 1760.

While various embodiments of the present invention have been describedin detail, it is apparent that further modifications and adaptations ofthe invention will occur to those skilled in the art. However, it is tobe expressly understood that such modifications and adaptations arewithin the spirit and scope of the present invention.

1. A method for use in converting content of electronic data from asource form to a target form, said electronic data having a machineformat and an informational content independent of said machine formatand any machine instructions, said method comprising the steps of:defining, by utilizing a computer, a transformation matrix for use by amachine tool involving: providing a set of source content elementsreflecting a source environment, wherein said source content elementsinclude human readable informational content entered by one or morehuman users in a form free from compliance with any device format, saidsource content elements reflecting inconsistencies of linguistics, atleast including different terms to identify same subject matter, andsyntax, at least including different ordering of terms; providing a setof normalized content elements that are amenable to transformation tothe target form; establishing a normalization structure for normalizingsaid set of source content elements to said set of normalized contentelements with respect to linguistics and syntax, wherein the sourcecontent elements correspond to a single normalized content element, andwherein said normalization structure is based on a knowledge basedeveloped from information about said set of source content elements,the establishing the normalization structure comprises utilizing grammarrules to identify one or more attributes of at least a subset of saidset of source content elements and utilizing linguistics rules toidentify attributes or attribute values of said source content elementsthat are expressed in a plurality of forms; defining a set of rules forconverting said normalized content elements to target content elements;receiving an item of electronic data having a machine format andinformation content including at least one source content element andextracting said information content using said machine format; using afirst operating of said machine tool to apply said transformation matrixby: identifying a first source content element under consideration;applying said normalization structure to said first source contentelement to identify a first normalized content element; and using saidset of rules with respect to said first normalized content element toconvert said first source content element to said target form, assistingin applying said normalization structure by associating contextualinformation with said normalized content elements, wherein saidassociating contextual information comprises providing tags forschematizing source information; and providing, by using secondoperating of said machine tool, an output including said first sourcecontent element converted to said target form.
 2. The method as setforth in claim 1, wherein one of said source content elements and saidtarget content elements comprises business content.
 3. The method as setforth in claim 1, wherein said step of applying said normalizationstructure comprises accessing a knowledge base developed from ananalysis of source content element.
 4. The method as set forth in claim1, wherein said normalization structure reflects a standardizedterminology and syntax for said source content elements.
 5. The methodas set forth in claim 1, wherein said step of establishing comprisesassisting in prioritizing individual source content elements fornormalization to said set of normalized content elements by performing astatistical analysis of said source content elements.
 6. The method asset forth in claim 1, further comprising the step of providing assistingin developing said normalization structure by providing a graphicalrepresentation of said normalization structure in development.
 7. Themethod as set forth in claim 1, further comprising the step of providingassisting in completing said normalization structure by providing agraphical user interface for identifying source content elements thathave been normalized to said set of normalized content elements.
 8. Themethod as set forth in claim 1, wherein said step of establishing anormalization structure comprises assisting in applying saidtransformation matrix by associating on textual information with saidnormalized content element.
 9. The method as set forth in claim 8,wherein said step of associating contextual information comprisesproviding tags for schematizing said source information.
 10. Anapparatus for use in converting content of electronic data from a sourceform to a target form, said electronic data having a machine format andan informational content independent of said machine format and anymachine instructions, said apparatus comprising: a storage medium forestablishing and storing a normalization structure for normalizingsource content elements with respect to linguistics and syntax tostandardized content elements amenable to a transformation to the targetform, and for storing a second conversion structure for converting saidstandardized content elements to target content elements of said targetform, wherein the source content elements correspond to a singlenormalized content element, wherein said source content elements includehuman informational content entered by one or more human users in a formfree from compliance with any device format, said source contentelements reflecting inconsistencies of linguistics, at least includingdifferent terms to identify same subject matter, and syntax, at leastincluding different ordering of terms, and wherein said normalizationstructure is based on a knowledge base developed from informationrelated to said set of source content elements, the establishing thenormalization structure comprises utilizing grammar rules to identifyone or more attributes of at least a subset of said set of sourcecontent elements and utilizing linguistics rules to identify attributesor attribute values of said source content elements that are expressedin a plurality of forms; an input structure for receiving input contenthaving the machine format and information content including a firstsource content element and for extracting said information content usingsaid machine format; a processor for accessing said normalizationstructure and said second conversion structure from said storage mediumand using said normalization structure and said second conversionstructure to convert said first source content element to at least onetarget content element; a first operating of said machine tool to applya transformation matrix by: applying said normalization structure tosaid first source content element to identify a first normalized contentelement; and using said set of rules with respect to said firstnormalized content element to convert said first source content elementto said target form; and the processor further for assisting in theapplying said normalization structure by associating contextualinformation with said normalized content elements, wherein saidassociating contextual information comprises providing tags forschematizing said source information; an output structure for providingan output including said at least one target content element.
 11. Theapparatus as set forth in claim 10, wherein one of said first sourcecontent element and said at least one target content element comprisesbusiness content.
 12. The apparatus as set forth in claim 10, whereinsaid processor is operative for accessing a knowledge base developedfrom an analysis of said source content element.
 13. The apparatus asset forth in claim 10, wherein said normalization structure reflects astandardized terminology and syntax for said source content elements.14. The apparatus as set forth in claim 10, wherein said processor isoperative for assisting in prioritizing individual source contentelements for normalization to said set of normalized content element byperforming a statistical analysis of said source content elements. 15.The apparatus as set forth in claim 10, further comprising a developmentmodule for use in establishing said normalization structure, saiddevelopment module being operative to assist in developing saidnormalization structure by providing a graphical representation of saidnormalization structure in development.
 16. The apparatus as set forthin claim 10, further comprising a development module operative forproviding a graphical user interface for assisting in completing saidnormalization structure by identifying source content elements that havebeen normalized to said set of normalized content elements.